{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-09-07T02:25:28.875496Z",
     "start_time": "2025-09-07T02:25:28.506947Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "plt.rcParams['font.family'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "import os\n",
    "# 关键：在导入sklearn相关模块前设置环境变量\n",
    "os.environ[\"OMP_NUM_THREADS\"] = \"2\"\n",
    "\"\"\"读取附件\"\"\"\n",
    "data = pd.read_excel('附件.xlsx')\n",
    "data"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        序号  孕妇代码  年龄     身高     体重                 末次月经 IVF妊娠  \\\n",
       "0        1  A001  31  160.0  72.00  2023-02-01 00:00:00  自然受孕   \n",
       "1        2  A001  31  160.0  73.00  2023-02-01 00:00:00  自然受孕   \n",
       "2        3  A001  31  160.0  73.00  2023-02-01 00:00:00  自然受孕   \n",
       "3        4  A001  31  160.0  74.00  2023-02-01 00:00:00  自然受孕   \n",
       "4        5  A002  32  149.0  74.00  2023-11-09 00:00:00  自然受孕   \n",
       "...    ...   ...  ..    ...    ...                  ...   ...   \n",
       "1077  1078  A266  30  159.0  83.35           2022-12-29  自然受孕   \n",
       "1078  1079  A267  28  155.0  73.76           2023-02-25  自然受孕   \n",
       "1079  1080  A267  28  155.0  74.06           2023-02-25  自然受孕   \n",
       "1080  1081  A267  28  155.0  74.74           2023-02-25  自然受孕   \n",
       "1081  1082  A267  28  155.0  75.85           2023-02-25  自然受孕   \n",
       "\n",
       "                     检测日期  检测抽血次数   检测孕周  ...    Y染色体浓度    X染色体浓度  \\\n",
       "0                20230429       1  11w+6  ...  0.025936  0.038061   \n",
       "1                20230531       2  15w+6  ...  0.034887  0.059572   \n",
       "2                20230625       3  20w+1  ...  0.066171  0.075995   \n",
       "3                20230716       4  22w+6  ...  0.061192  0.052305   \n",
       "4                20240219       1  13w+6  ...  0.059230  0.059708   \n",
       "...                   ...     ...    ...  ...       ...       ...   \n",
       "1077  2023-05-02 00:00:00       4  17w+5  ...  0.099052  0.056686   \n",
       "1078  2023-05-17 00:00:00       1  11w+4  ...  0.098706  0.023663   \n",
       "1079  2023-05-24 00:00:00       2  12w+4  ...  0.102088  0.080264   \n",
       "1080  2023-05-31 00:00:00       3  13w+4  ...  0.109855  0.074050   \n",
       "1081  2023-06-07 00:00:00       4  14w+4  ...  0.122668  0.212257   \n",
       "\n",
       "      13号染色体的GC含量  18号染色体的GC含量  21号染色体的GC含量  被过滤掉读段数的比例  染色体的非整倍体  怀孕次数  生产次数  \\\n",
       "0        0.377069     0.389803     0.399399    0.027484       NaN     1     0   \n",
       "1        0.371542     0.384771     0.391706    0.019617       NaN     1     0   \n",
       "2        0.377449     0.390582     0.399480    0.022312       NaN     1     0   \n",
       "3        0.375613     0.389251     0.397212    0.023280       NaN     1     0   \n",
       "4        0.380260     0.393618     0.404868    0.024212       NaN     2     1   \n",
       "...           ...          ...          ...         ...       ...   ...   ...   \n",
       "1077     0.376861     0.389914     0.397090    0.017951       T18     1     0   \n",
       "1078     0.377597     0.387901     0.404293    0.022549       T21     1     0   \n",
       "1079     0.379041     0.391748     0.400433    0.021330       NaN     1     0   \n",
       "1080     0.379107     0.388544     0.401030    0.022013       NaN     1     0   \n",
       "1081     0.379130     0.391535     0.401856    0.016906       NaN     1     0   \n",
       "\n",
       "      胎儿是否健康  \n",
       "0          是  \n",
       "1          是  \n",
       "2          是  \n",
       "3          是  \n",
       "4          否  \n",
       "...      ...  \n",
       "1077       是  \n",
       "1078       是  \n",
       "1079       是  \n",
       "1080       是  \n",
       "1081       是  \n",
       "\n",
       "[1082 rows x 31 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>序号</th>\n",
       "      <th>孕妇代码</th>\n",
       "      <th>年龄</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>末次月经</th>\n",
       "      <th>IVF妊娠</th>\n",
       "      <th>检测日期</th>\n",
       "      <th>检测抽血次数</th>\n",
       "      <th>检测孕周</th>\n",
       "      <th>...</th>\n",
       "      <th>Y染色体浓度</th>\n",
       "      <th>X染色体浓度</th>\n",
       "      <th>13号染色体的GC含量</th>\n",
       "      <th>18号染色体的GC含量</th>\n",
       "      <th>21号染色体的GC含量</th>\n",
       "      <th>被过滤掉读段数的比例</th>\n",
       "      <th>染色体的非整倍体</th>\n",
       "      <th>怀孕次数</th>\n",
       "      <th>生产次数</th>\n",
       "      <th>胎儿是否健康</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A001</td>\n",
       "      <td>31</td>\n",
       "      <td>160.0</td>\n",
       "      <td>72.00</td>\n",
       "      <td>2023-02-01 00:00:00</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>20230429</td>\n",
       "      <td>1</td>\n",
       "      <td>11w+6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025936</td>\n",
       "      <td>0.038061</td>\n",
       "      <td>0.377069</td>\n",
       "      <td>0.389803</td>\n",
       "      <td>0.399399</td>\n",
       "      <td>0.027484</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>A001</td>\n",
       "      <td>31</td>\n",
       "      <td>160.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>2023-02-01 00:00:00</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>20230531</td>\n",
       "      <td>2</td>\n",
       "      <td>15w+6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.034887</td>\n",
       "      <td>0.059572</td>\n",
       "      <td>0.371542</td>\n",
       "      <td>0.384771</td>\n",
       "      <td>0.391706</td>\n",
       "      <td>0.019617</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>A001</td>\n",
       "      <td>31</td>\n",
       "      <td>160.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>2023-02-01 00:00:00</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>20230625</td>\n",
       "      <td>3</td>\n",
       "      <td>20w+1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.066171</td>\n",
       "      <td>0.075995</td>\n",
       "      <td>0.377449</td>\n",
       "      <td>0.390582</td>\n",
       "      <td>0.399480</td>\n",
       "      <td>0.022312</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>A001</td>\n",
       "      <td>31</td>\n",
       "      <td>160.0</td>\n",
       "      <td>74.00</td>\n",
       "      <td>2023-02-01 00:00:00</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>20230716</td>\n",
       "      <td>4</td>\n",
       "      <td>22w+6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.061192</td>\n",
       "      <td>0.052305</td>\n",
       "      <td>0.375613</td>\n",
       "      <td>0.389251</td>\n",
       "      <td>0.397212</td>\n",
       "      <td>0.023280</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>A002</td>\n",
       "      <td>32</td>\n",
       "      <td>149.0</td>\n",
       "      <td>74.00</td>\n",
       "      <td>2023-11-09 00:00:00</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>20240219</td>\n",
       "      <td>1</td>\n",
       "      <td>13w+6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.059230</td>\n",
       "      <td>0.059708</td>\n",
       "      <td>0.380260</td>\n",
       "      <td>0.393618</td>\n",
       "      <td>0.404868</td>\n",
       "      <td>0.024212</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1077</th>\n",
       "      <td>1078</td>\n",
       "      <td>A266</td>\n",
       "      <td>30</td>\n",
       "      <td>159.0</td>\n",
       "      <td>83.35</td>\n",
       "      <td>2022-12-29</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>2023-05-02 00:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>17w+5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.099052</td>\n",
       "      <td>0.056686</td>\n",
       "      <td>0.376861</td>\n",
       "      <td>0.389914</td>\n",
       "      <td>0.397090</td>\n",
       "      <td>0.017951</td>\n",
       "      <td>T18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1078</th>\n",
       "      <td>1079</td>\n",
       "      <td>A267</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>73.76</td>\n",
       "      <td>2023-02-25</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>2023-05-17 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>11w+4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.098706</td>\n",
       "      <td>0.023663</td>\n",
       "      <td>0.377597</td>\n",
       "      <td>0.387901</td>\n",
       "      <td>0.404293</td>\n",
       "      <td>0.022549</td>\n",
       "      <td>T21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1079</th>\n",
       "      <td>1080</td>\n",
       "      <td>A267</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>74.06</td>\n",
       "      <td>2023-02-25</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>2023-05-24 00:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>12w+4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.102088</td>\n",
       "      <td>0.080264</td>\n",
       "      <td>0.379041</td>\n",
       "      <td>0.391748</td>\n",
       "      <td>0.400433</td>\n",
       "      <td>0.021330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1080</th>\n",
       "      <td>1081</td>\n",
       "      <td>A267</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>74.74</td>\n",
       "      <td>2023-02-25</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>2023-05-31 00:00:00</td>\n",
       "      <td>3</td>\n",
       "      <td>13w+4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.109855</td>\n",
       "      <td>0.074050</td>\n",
       "      <td>0.379107</td>\n",
       "      <td>0.388544</td>\n",
       "      <td>0.401030</td>\n",
       "      <td>0.022013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081</th>\n",
       "      <td>1082</td>\n",
       "      <td>A267</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>75.85</td>\n",
       "      <td>2023-02-25</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>2023-06-07 00:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>14w+4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.122668</td>\n",
       "      <td>0.212257</td>\n",
       "      <td>0.379130</td>\n",
       "      <td>0.391535</td>\n",
       "      <td>0.401856</td>\n",
       "      <td>0.016906</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1082 rows × 31 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 213
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T02:25:28.926434Z",
     "start_time": "2025-09-07T02:25:28.912716Z"
    }
   },
   "cell_type": "code",
   "source": [
    "select_cols = ['孕妇代码','年龄','身高','体重','孕妇BMI','Y染色体浓度','检测孕周','13号染色体的GC含量','18号染色体的GC含量','21号染色体的GC含量','IVF妊娠']\n",
    "origin_df = data[select_cols].copy()\n",
    "origin_df"
   ],
   "id": "64f7d75967d1c6c1",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      孕妇代码  年龄     身高     体重      孕妇BMI    Y染色体浓度   检测孕周  13号染色体的GC含量  \\\n",
       "0     A001  31  160.0  72.00  28.125000  0.025936  11w+6     0.377069   \n",
       "1     A001  31  160.0  73.00  28.515625  0.034887  15w+6     0.371542   \n",
       "2     A001  31  160.0  73.00  28.515625  0.066171  20w+1     0.377449   \n",
       "3     A001  31  160.0  74.00  28.906250  0.061192  22w+6     0.375613   \n",
       "4     A002  32  149.0  74.00  33.331832  0.059230  13w+6     0.380260   \n",
       "...    ...  ..    ...    ...        ...       ...    ...          ...   \n",
       "1077  A266  30  159.0  83.35  32.969881  0.099052  17w+5     0.376861   \n",
       "1078  A267  28  155.0  73.76  30.703133  0.098706  11w+4     0.377597   \n",
       "1079  A267  28  155.0  74.06  30.825814  0.102088  12w+4     0.379041   \n",
       "1080  A267  28  155.0  74.74  31.107551  0.109855  13w+4     0.379107   \n",
       "1081  A267  28  155.0  75.85  31.572341  0.122668  14w+4     0.379130   \n",
       "\n",
       "      18号染色体的GC含量  21号染色体的GC含量 IVF妊娠  \n",
       "0        0.389803     0.399399  自然受孕  \n",
       "1        0.384771     0.391706  自然受孕  \n",
       "2        0.390582     0.399480  自然受孕  \n",
       "3        0.389251     0.397212  自然受孕  \n",
       "4        0.393618     0.404868  自然受孕  \n",
       "...           ...          ...   ...  \n",
       "1077     0.389914     0.397090  自然受孕  \n",
       "1078     0.387901     0.404293  自然受孕  \n",
       "1079     0.391748     0.400433  自然受孕  \n",
       "1080     0.388544     0.401030  自然受孕  \n",
       "1081     0.391535     0.401856  自然受孕  \n",
       "\n",
       "[1082 rows x 11 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>孕妇代码</th>\n",
       "      <th>年龄</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>孕妇BMI</th>\n",
       "      <th>Y染色体浓度</th>\n",
       "      <th>检测孕周</th>\n",
       "      <th>13号染色体的GC含量</th>\n",
       "      <th>18号染色体的GC含量</th>\n",
       "      <th>21号染色体的GC含量</th>\n",
       "      <th>IVF妊娠</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A001</td>\n",
       "      <td>31</td>\n",
       "      <td>160.0</td>\n",
       "      <td>72.00</td>\n",
       "      <td>28.125000</td>\n",
       "      <td>0.025936</td>\n",
       "      <td>11w+6</td>\n",
       "      <td>0.377069</td>\n",
       "      <td>0.389803</td>\n",
       "      <td>0.399399</td>\n",
       "      <td>自然受孕</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A001</td>\n",
       "      <td>31</td>\n",
       "      <td>160.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>28.515625</td>\n",
       "      <td>0.034887</td>\n",
       "      <td>15w+6</td>\n",
       "      <td>0.371542</td>\n",
       "      <td>0.384771</td>\n",
       "      <td>0.391706</td>\n",
       "      <td>自然受孕</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A001</td>\n",
       "      <td>31</td>\n",
       "      <td>160.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>28.515625</td>\n",
       "      <td>0.066171</td>\n",
       "      <td>20w+1</td>\n",
       "      <td>0.377449</td>\n",
       "      <td>0.390582</td>\n",
       "      <td>0.399480</td>\n",
       "      <td>自然受孕</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A001</td>\n",
       "      <td>31</td>\n",
       "      <td>160.0</td>\n",
       "      <td>74.00</td>\n",
       "      <td>28.906250</td>\n",
       "      <td>0.061192</td>\n",
       "      <td>22w+6</td>\n",
       "      <td>0.375613</td>\n",
       "      <td>0.389251</td>\n",
       "      <td>0.397212</td>\n",
       "      <td>自然受孕</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A002</td>\n",
       "      <td>32</td>\n",
       "      <td>149.0</td>\n",
       "      <td>74.00</td>\n",
       "      <td>33.331832</td>\n",
       "      <td>0.059230</td>\n",
       "      <td>13w+6</td>\n",
       "      <td>0.380260</td>\n",
       "      <td>0.393618</td>\n",
       "      <td>0.404868</td>\n",
       "      <td>自然受孕</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1077</th>\n",
       "      <td>A266</td>\n",
       "      <td>30</td>\n",
       "      <td>159.0</td>\n",
       "      <td>83.35</td>\n",
       "      <td>32.969881</td>\n",
       "      <td>0.099052</td>\n",
       "      <td>17w+5</td>\n",
       "      <td>0.376861</td>\n",
       "      <td>0.389914</td>\n",
       "      <td>0.397090</td>\n",
       "      <td>自然受孕</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1078</th>\n",
       "      <td>A267</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>73.76</td>\n",
       "      <td>30.703133</td>\n",
       "      <td>0.098706</td>\n",
       "      <td>11w+4</td>\n",
       "      <td>0.377597</td>\n",
       "      <td>0.387901</td>\n",
       "      <td>0.404293</td>\n",
       "      <td>自然受孕</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1079</th>\n",
       "      <td>A267</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>74.06</td>\n",
       "      <td>30.825814</td>\n",
       "      <td>0.102088</td>\n",
       "      <td>12w+4</td>\n",
       "      <td>0.379041</td>\n",
       "      <td>0.391748</td>\n",
       "      <td>0.400433</td>\n",
       "      <td>自然受孕</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1080</th>\n",
       "      <td>A267</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>74.74</td>\n",
       "      <td>31.107551</td>\n",
       "      <td>0.109855</td>\n",
       "      <td>13w+4</td>\n",
       "      <td>0.379107</td>\n",
       "      <td>0.388544</td>\n",
       "      <td>0.401030</td>\n",
       "      <td>自然受孕</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081</th>\n",
       "      <td>A267</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>75.85</td>\n",
       "      <td>31.572341</td>\n",
       "      <td>0.122668</td>\n",
       "      <td>14w+4</td>\n",
       "      <td>0.379130</td>\n",
       "      <td>0.391535</td>\n",
       "      <td>0.401856</td>\n",
       "      <td>自然受孕</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1082 rows × 11 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 214
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T02:25:29.175876Z",
     "start_time": "2025-09-07T02:25:29.134118Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 计算每个孕妇代码下 Y染色体浓度 > 0.04 的比例\n",
    "y_concentration_ratio = (\n",
    "    origin_df.groupby('孕妇代码')['Y染色体浓度']\n",
    "    .apply(lambda x: (x > 0.04).sum() / len(x))  # 超过0.04的比例\n",
    "    .rename('ratio over 0.04')\n",
    ")\n",
    "\n",
    "# 将结果合并回 origin_df\n",
    "origin_df = origin_df.merge(y_concentration_ratio, on='孕妇代码', how='left')\n",
    "origin_df"
   ],
   "id": "c406e24c7856c745",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      孕妇代码  年龄     身高     体重      孕妇BMI    Y染色体浓度   检测孕周  13号染色体的GC含量  \\\n",
       "0     A001  31  160.0  72.00  28.125000  0.025936  11w+6     0.377069   \n",
       "1     A001  31  160.0  73.00  28.515625  0.034887  15w+6     0.371542   \n",
       "2     A001  31  160.0  73.00  28.515625  0.066171  20w+1     0.377449   \n",
       "3     A001  31  160.0  74.00  28.906250  0.061192  22w+6     0.375613   \n",
       "4     A002  32  149.0  74.00  33.331832  0.059230  13w+6     0.380260   \n",
       "...    ...  ..    ...    ...        ...       ...    ...          ...   \n",
       "1077  A266  30  159.0  83.35  32.969881  0.099052  17w+5     0.376861   \n",
       "1078  A267  28  155.0  73.76  30.703133  0.098706  11w+4     0.377597   \n",
       "1079  A267  28  155.0  74.06  30.825814  0.102088  12w+4     0.379041   \n",
       "1080  A267  28  155.0  74.74  31.107551  0.109855  13w+4     0.379107   \n",
       "1081  A267  28  155.0  75.85  31.572341  0.122668  14w+4     0.379130   \n",
       "\n",
       "      18号染色体的GC含量  21号染色体的GC含量 IVF妊娠  ratio over 0.04  \n",
       "0        0.389803     0.399399  自然受孕              0.5  \n",
       "1        0.384771     0.391706  自然受孕              0.5  \n",
       "2        0.390582     0.399480  自然受孕              0.5  \n",
       "3        0.389251     0.397212  自然受孕              0.5  \n",
       "4        0.393618     0.404868  自然受孕              1.0  \n",
       "...           ...          ...   ...              ...  \n",
       "1077     0.389914     0.397090  自然受孕              1.0  \n",
       "1078     0.387901     0.404293  自然受孕              1.0  \n",
       "1079     0.391748     0.400433  自然受孕              1.0  \n",
       "1080     0.388544     0.401030  自然受孕              1.0  \n",
       "1081     0.391535     0.401856  自然受孕              1.0  \n",
       "\n",
       "[1082 rows x 12 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>孕妇代码</th>\n",
       "      <th>年龄</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>孕妇BMI</th>\n",
       "      <th>Y染色体浓度</th>\n",
       "      <th>检测孕周</th>\n",
       "      <th>13号染色体的GC含量</th>\n",
       "      <th>18号染色体的GC含量</th>\n",
       "      <th>21号染色体的GC含量</th>\n",
       "      <th>IVF妊娠</th>\n",
       "      <th>ratio over 0.04</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A001</td>\n",
       "      <td>31</td>\n",
       "      <td>160.0</td>\n",
       "      <td>72.00</td>\n",
       "      <td>28.125000</td>\n",
       "      <td>0.025936</td>\n",
       "      <td>11w+6</td>\n",
       "      <td>0.377069</td>\n",
       "      <td>0.389803</td>\n",
       "      <td>0.399399</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A001</td>\n",
       "      <td>31</td>\n",
       "      <td>160.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>28.515625</td>\n",
       "      <td>0.034887</td>\n",
       "      <td>15w+6</td>\n",
       "      <td>0.371542</td>\n",
       "      <td>0.384771</td>\n",
       "      <td>0.391706</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A001</td>\n",
       "      <td>31</td>\n",
       "      <td>160.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>28.515625</td>\n",
       "      <td>0.066171</td>\n",
       "      <td>20w+1</td>\n",
       "      <td>0.377449</td>\n",
       "      <td>0.390582</td>\n",
       "      <td>0.399480</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A001</td>\n",
       "      <td>31</td>\n",
       "      <td>160.0</td>\n",
       "      <td>74.00</td>\n",
       "      <td>28.906250</td>\n",
       "      <td>0.061192</td>\n",
       "      <td>22w+6</td>\n",
       "      <td>0.375613</td>\n",
       "      <td>0.389251</td>\n",
       "      <td>0.397212</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A002</td>\n",
       "      <td>32</td>\n",
       "      <td>149.0</td>\n",
       "      <td>74.00</td>\n",
       "      <td>33.331832</td>\n",
       "      <td>0.059230</td>\n",
       "      <td>13w+6</td>\n",
       "      <td>0.380260</td>\n",
       "      <td>0.393618</td>\n",
       "      <td>0.404868</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1077</th>\n",
       "      <td>A266</td>\n",
       "      <td>30</td>\n",
       "      <td>159.0</td>\n",
       "      <td>83.35</td>\n",
       "      <td>32.969881</td>\n",
       "      <td>0.099052</td>\n",
       "      <td>17w+5</td>\n",
       "      <td>0.376861</td>\n",
       "      <td>0.389914</td>\n",
       "      <td>0.397090</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1078</th>\n",
       "      <td>A267</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>73.76</td>\n",
       "      <td>30.703133</td>\n",
       "      <td>0.098706</td>\n",
       "      <td>11w+4</td>\n",
       "      <td>0.377597</td>\n",
       "      <td>0.387901</td>\n",
       "      <td>0.404293</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1079</th>\n",
       "      <td>A267</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>74.06</td>\n",
       "      <td>30.825814</td>\n",
       "      <td>0.102088</td>\n",
       "      <td>12w+4</td>\n",
       "      <td>0.379041</td>\n",
       "      <td>0.391748</td>\n",
       "      <td>0.400433</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1080</th>\n",
       "      <td>A267</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>74.74</td>\n",
       "      <td>31.107551</td>\n",
       "      <td>0.109855</td>\n",
       "      <td>13w+4</td>\n",
       "      <td>0.379107</td>\n",
       "      <td>0.388544</td>\n",
       "      <td>0.401030</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081</th>\n",
       "      <td>A267</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>75.85</td>\n",
       "      <td>31.572341</td>\n",
       "      <td>0.122668</td>\n",
       "      <td>14w+4</td>\n",
       "      <td>0.379130</td>\n",
       "      <td>0.391535</td>\n",
       "      <td>0.401856</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1082 rows × 12 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 215
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T02:25:29.587425Z",
     "start_time": "2025-09-07T02:25:29.569492Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 对数据进行预处理\n",
    "# 定义一个周数转化函数\n",
    "import re\n",
    "def convert_week(week_str):\n",
    "    try:\n",
    "        # 处理空值\n",
    "        if pd.isna(week_str):\n",
    "            return None\n",
    "\n",
    "        # 统一转为小写并清理格式\n",
    "        week_str = str(week_str).lower()\n",
    "        # 移除所有非数字和w的字符\n",
    "        week_str = re.sub(r'[^0-9.w]', '', week_str)\n",
    "        # 处理连续的w\n",
    "        week_str = re.sub(r'w+', 'w', week_str)\n",
    "        parts = week_str.split('w')\n",
    "        # 处理不同格式\n",
    "        if len(parts) == 2 and parts[1] != '':\n",
    "            # 处理类似 \"12w3\" 格式\n",
    "            return float(parts[0]) + float(parts[1])/7.0\n",
    "        elif len(parts) >= 1 and parts[0] != '':\n",
    "            # 处理只有周数的情况，如 \"12\" 或 \"12w\"\n",
    "            return float(parts[0])\n",
    "        else:\n",
    "            # 无法解析的格式\n",
    "            return None\n",
    "    except:\n",
    "        # 处理转换错误\n",
    "        return None\n",
    "# 将周数转化一下\n",
    "origin_df['检测孕周'] = origin_df['检测孕周'].apply(convert_week)\n",
    "origin_df"
   ],
   "id": "9360511b9a987d62",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      孕妇代码  年龄     身高     体重      孕妇BMI    Y染色体浓度       检测孕周  13号染色体的GC含量  \\\n",
       "0     A001  31  160.0  72.00  28.125000  0.025936  11.857143     0.377069   \n",
       "1     A001  31  160.0  73.00  28.515625  0.034887  15.857143     0.371542   \n",
       "2     A001  31  160.0  73.00  28.515625  0.066171  20.142857     0.377449   \n",
       "3     A001  31  160.0  74.00  28.906250  0.061192  22.857143     0.375613   \n",
       "4     A002  32  149.0  74.00  33.331832  0.059230  13.857143     0.380260   \n",
       "...    ...  ..    ...    ...        ...       ...        ...          ...   \n",
       "1077  A266  30  159.0  83.35  32.969881  0.099052  17.714286     0.376861   \n",
       "1078  A267  28  155.0  73.76  30.703133  0.098706  11.571429     0.377597   \n",
       "1079  A267  28  155.0  74.06  30.825814  0.102088  12.571429     0.379041   \n",
       "1080  A267  28  155.0  74.74  31.107551  0.109855  13.571429     0.379107   \n",
       "1081  A267  28  155.0  75.85  31.572341  0.122668  14.571429     0.379130   \n",
       "\n",
       "      18号染色体的GC含量  21号染色体的GC含量 IVF妊娠  ratio over 0.04  \n",
       "0        0.389803     0.399399  自然受孕              0.5  \n",
       "1        0.384771     0.391706  自然受孕              0.5  \n",
       "2        0.390582     0.399480  自然受孕              0.5  \n",
       "3        0.389251     0.397212  自然受孕              0.5  \n",
       "4        0.393618     0.404868  自然受孕              1.0  \n",
       "...           ...          ...   ...              ...  \n",
       "1077     0.389914     0.397090  自然受孕              1.0  \n",
       "1078     0.387901     0.404293  自然受孕              1.0  \n",
       "1079     0.391748     0.400433  自然受孕              1.0  \n",
       "1080     0.388544     0.401030  自然受孕              1.0  \n",
       "1081     0.391535     0.401856  自然受孕              1.0  \n",
       "\n",
       "[1082 rows x 12 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>孕妇代码</th>\n",
       "      <th>年龄</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>孕妇BMI</th>\n",
       "      <th>Y染色体浓度</th>\n",
       "      <th>检测孕周</th>\n",
       "      <th>13号染色体的GC含量</th>\n",
       "      <th>18号染色体的GC含量</th>\n",
       "      <th>21号染色体的GC含量</th>\n",
       "      <th>IVF妊娠</th>\n",
       "      <th>ratio over 0.04</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A001</td>\n",
       "      <td>31</td>\n",
       "      <td>160.0</td>\n",
       "      <td>72.00</td>\n",
       "      <td>28.125000</td>\n",
       "      <td>0.025936</td>\n",
       "      <td>11.857143</td>\n",
       "      <td>0.377069</td>\n",
       "      <td>0.389803</td>\n",
       "      <td>0.399399</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A001</td>\n",
       "      <td>31</td>\n",
       "      <td>160.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>28.515625</td>\n",
       "      <td>0.034887</td>\n",
       "      <td>15.857143</td>\n",
       "      <td>0.371542</td>\n",
       "      <td>0.384771</td>\n",
       "      <td>0.391706</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A001</td>\n",
       "      <td>31</td>\n",
       "      <td>160.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>28.515625</td>\n",
       "      <td>0.066171</td>\n",
       "      <td>20.142857</td>\n",
       "      <td>0.377449</td>\n",
       "      <td>0.390582</td>\n",
       "      <td>0.399480</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A001</td>\n",
       "      <td>31</td>\n",
       "      <td>160.0</td>\n",
       "      <td>74.00</td>\n",
       "      <td>28.906250</td>\n",
       "      <td>0.061192</td>\n",
       "      <td>22.857143</td>\n",
       "      <td>0.375613</td>\n",
       "      <td>0.389251</td>\n",
       "      <td>0.397212</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A002</td>\n",
       "      <td>32</td>\n",
       "      <td>149.0</td>\n",
       "      <td>74.00</td>\n",
       "      <td>33.331832</td>\n",
       "      <td>0.059230</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>0.380260</td>\n",
       "      <td>0.393618</td>\n",
       "      <td>0.404868</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1077</th>\n",
       "      <td>A266</td>\n",
       "      <td>30</td>\n",
       "      <td>159.0</td>\n",
       "      <td>83.35</td>\n",
       "      <td>32.969881</td>\n",
       "      <td>0.099052</td>\n",
       "      <td>17.714286</td>\n",
       "      <td>0.376861</td>\n",
       "      <td>0.389914</td>\n",
       "      <td>0.397090</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1078</th>\n",
       "      <td>A267</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>73.76</td>\n",
       "      <td>30.703133</td>\n",
       "      <td>0.098706</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>0.377597</td>\n",
       "      <td>0.387901</td>\n",
       "      <td>0.404293</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1079</th>\n",
       "      <td>A267</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>74.06</td>\n",
       "      <td>30.825814</td>\n",
       "      <td>0.102088</td>\n",
       "      <td>12.571429</td>\n",
       "      <td>0.379041</td>\n",
       "      <td>0.391748</td>\n",
       "      <td>0.400433</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1080</th>\n",
       "      <td>A267</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>74.74</td>\n",
       "      <td>31.107551</td>\n",
       "      <td>0.109855</td>\n",
       "      <td>13.571429</td>\n",
       "      <td>0.379107</td>\n",
       "      <td>0.388544</td>\n",
       "      <td>0.401030</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081</th>\n",
       "      <td>A267</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>75.85</td>\n",
       "      <td>31.572341</td>\n",
       "      <td>0.122668</td>\n",
       "      <td>14.571429</td>\n",
       "      <td>0.379130</td>\n",
       "      <td>0.391535</td>\n",
       "      <td>0.401856</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1082 rows × 12 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 216
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T02:25:30.842313Z",
     "start_time": "2025-09-07T02:25:30.820740Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 筛选出Y染色体浓度≥0.04的记录\n",
    "qualified_records = origin_df[origin_df['Y染色体浓度'] >= 0.04]\n",
    "# 按孕妇代码分组，找出每组中最小的检测孕周（即最早时间）\n",
    "early_detection = qualified_records.groupby('孕妇代码')['检测孕周'].min().reset_index()\n",
    "# 重命名列名使其更清晰\n",
    "early_detection = early_detection.rename(columns={'检测孕周': '最早达到目标Y染色体浓度的孕周'})\n",
    "# 如果需要将结果与原始数据中的其他信息合并（如BMI、胎儿健康状况）\n",
    "processed_df = pd.merge(early_detection, origin_df[['孕妇代码','年龄','身高','体重','孕妇BMI','Y染色体浓度','检测孕周','13号染色体的GC含量','18号染色体的GC含量','21号染色体的GC含量','IVF妊娠','ratio over 0.04']].drop_duplicates(),\n",
    "                     on='孕妇代码', how='left')\n",
    "processed_df = processed_df[processed_df['检测孕周']>=processed_df['最早达到目标Y染色体浓度的孕周']]\n",
    "processed_df"
   ],
   "id": "b32a108b66777a0e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      孕妇代码  最早达到目标Y染色体浓度的孕周  年龄     身高     体重      孕妇BMI    Y染色体浓度       检测孕周  \\\n",
       "2     A001        20.142857  31  160.0  73.00  28.515625  0.066171  20.142857   \n",
       "3     A001        20.142857  31  160.0  74.00  28.906250  0.061192  22.857143   \n",
       "4     A002        13.857143  32  149.0  74.00  33.331832  0.059230  13.857143   \n",
       "5     A002        13.857143  32  149.0  76.00  34.232692  0.042401  16.714286   \n",
       "6     A002        13.857143  32  149.0  75.00  33.782262  0.047825  19.714286   \n",
       "...    ...              ...  ..    ...    ...        ...       ...        ...   \n",
       "1053  A266        13.714286  30  159.0  83.35  32.969881  0.099052  17.714286   \n",
       "1054  A267        11.571429  28  155.0  73.76  30.703133  0.098706  11.571429   \n",
       "1055  A267        11.571429  28  155.0  74.06  30.825814  0.102088  12.571429   \n",
       "1056  A267        11.571429  28  155.0  74.74  31.107551  0.109855  13.571429   \n",
       "1057  A267        11.571429  28  155.0  75.85  31.572341  0.122668  14.571429   \n",
       "\n",
       "      13号染色体的GC含量  18号染色体的GC含量  21号染色体的GC含量 IVF妊娠  ratio over 0.04  \n",
       "2        0.377449     0.390582     0.399480  自然受孕              0.5  \n",
       "3        0.375613     0.389251     0.397212  自然受孕              0.5  \n",
       "4        0.380260     0.393618     0.404868  自然受孕              1.0  \n",
       "5        0.377387     0.390282     0.399467  自然受孕              1.0  \n",
       "6        0.375591     0.387779     0.395050  自然受孕              1.0  \n",
       "...           ...          ...          ...   ...              ...  \n",
       "1053     0.376861     0.389914     0.397090  自然受孕              1.0  \n",
       "1054     0.377597     0.387901     0.404293  自然受孕              1.0  \n",
       "1055     0.379041     0.391748     0.400433  自然受孕              1.0  \n",
       "1056     0.379107     0.388544     0.401030  自然受孕              1.0  \n",
       "1057     0.379130     0.391535     0.401856  自然受孕              1.0  \n",
       "\n",
       "[990 rows x 13 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>孕妇代码</th>\n",
       "      <th>最早达到目标Y染色体浓度的孕周</th>\n",
       "      <th>年龄</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>孕妇BMI</th>\n",
       "      <th>Y染色体浓度</th>\n",
       "      <th>检测孕周</th>\n",
       "      <th>13号染色体的GC含量</th>\n",
       "      <th>18号染色体的GC含量</th>\n",
       "      <th>21号染色体的GC含量</th>\n",
       "      <th>IVF妊娠</th>\n",
       "      <th>ratio over 0.04</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A001</td>\n",
       "      <td>20.142857</td>\n",
       "      <td>31</td>\n",
       "      <td>160.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>28.515625</td>\n",
       "      <td>0.066171</td>\n",
       "      <td>20.142857</td>\n",
       "      <td>0.377449</td>\n",
       "      <td>0.390582</td>\n",
       "      <td>0.399480</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A001</td>\n",
       "      <td>20.142857</td>\n",
       "      <td>31</td>\n",
       "      <td>160.0</td>\n",
       "      <td>74.00</td>\n",
       "      <td>28.906250</td>\n",
       "      <td>0.061192</td>\n",
       "      <td>22.857143</td>\n",
       "      <td>0.375613</td>\n",
       "      <td>0.389251</td>\n",
       "      <td>0.397212</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A002</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>32</td>\n",
       "      <td>149.0</td>\n",
       "      <td>74.00</td>\n",
       "      <td>33.331832</td>\n",
       "      <td>0.059230</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>0.380260</td>\n",
       "      <td>0.393618</td>\n",
       "      <td>0.404868</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A002</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>32</td>\n",
       "      <td>149.0</td>\n",
       "      <td>76.00</td>\n",
       "      <td>34.232692</td>\n",
       "      <td>0.042401</td>\n",
       "      <td>16.714286</td>\n",
       "      <td>0.377387</td>\n",
       "      <td>0.390282</td>\n",
       "      <td>0.399467</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A002</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>32</td>\n",
       "      <td>149.0</td>\n",
       "      <td>75.00</td>\n",
       "      <td>33.782262</td>\n",
       "      <td>0.047825</td>\n",
       "      <td>19.714286</td>\n",
       "      <td>0.375591</td>\n",
       "      <td>0.387779</td>\n",
       "      <td>0.395050</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1053</th>\n",
       "      <td>A266</td>\n",
       "      <td>13.714286</td>\n",
       "      <td>30</td>\n",
       "      <td>159.0</td>\n",
       "      <td>83.35</td>\n",
       "      <td>32.969881</td>\n",
       "      <td>0.099052</td>\n",
       "      <td>17.714286</td>\n",
       "      <td>0.376861</td>\n",
       "      <td>0.389914</td>\n",
       "      <td>0.397090</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1054</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>73.76</td>\n",
       "      <td>30.703133</td>\n",
       "      <td>0.098706</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>0.377597</td>\n",
       "      <td>0.387901</td>\n",
       "      <td>0.404293</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>74.06</td>\n",
       "      <td>30.825814</td>\n",
       "      <td>0.102088</td>\n",
       "      <td>12.571429</td>\n",
       "      <td>0.379041</td>\n",
       "      <td>0.391748</td>\n",
       "      <td>0.400433</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>74.74</td>\n",
       "      <td>31.107551</td>\n",
       "      <td>0.109855</td>\n",
       "      <td>13.571429</td>\n",
       "      <td>0.379107</td>\n",
       "      <td>0.388544</td>\n",
       "      <td>0.401030</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1057</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>75.85</td>\n",
       "      <td>31.572341</td>\n",
       "      <td>0.122668</td>\n",
       "      <td>14.571429</td>\n",
       "      <td>0.379130</td>\n",
       "      <td>0.391535</td>\n",
       "      <td>0.401856</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>990 rows × 13 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 217
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T02:25:31.055857Z",
     "start_time": "2025-09-07T02:25:31.030344Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 创建一个映射字典\n",
    "ivf_mapping = {\n",
    "    '自然受孕': 0,\n",
    "    'IUI（人工授精）': 1,\n",
    "    'IVF（试管婴儿）': 2\n",
    "}\n",
    "# 使用map函数进行编码\n",
    "processed_df['IVF妊娠_encoded'] = processed_df['IVF妊娠'].map(ivf_mapping)\n",
    "\n",
    "# 查看结果\n",
    "processed_df"
   ],
   "id": "efb52592465c3a80",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      孕妇代码  最早达到目标Y染色体浓度的孕周  年龄     身高     体重      孕妇BMI    Y染色体浓度       检测孕周  \\\n",
       "2     A001        20.142857  31  160.0  73.00  28.515625  0.066171  20.142857   \n",
       "3     A001        20.142857  31  160.0  74.00  28.906250  0.061192  22.857143   \n",
       "4     A002        13.857143  32  149.0  74.00  33.331832  0.059230  13.857143   \n",
       "5     A002        13.857143  32  149.0  76.00  34.232692  0.042401  16.714286   \n",
       "6     A002        13.857143  32  149.0  75.00  33.782262  0.047825  19.714286   \n",
       "...    ...              ...  ..    ...    ...        ...       ...        ...   \n",
       "1053  A266        13.714286  30  159.0  83.35  32.969881  0.099052  17.714286   \n",
       "1054  A267        11.571429  28  155.0  73.76  30.703133  0.098706  11.571429   \n",
       "1055  A267        11.571429  28  155.0  74.06  30.825814  0.102088  12.571429   \n",
       "1056  A267        11.571429  28  155.0  74.74  31.107551  0.109855  13.571429   \n",
       "1057  A267        11.571429  28  155.0  75.85  31.572341  0.122668  14.571429   \n",
       "\n",
       "      13号染色体的GC含量  18号染色体的GC含量  21号染色体的GC含量 IVF妊娠  ratio over 0.04  \\\n",
       "2        0.377449     0.390582     0.399480  自然受孕              0.5   \n",
       "3        0.375613     0.389251     0.397212  自然受孕              0.5   \n",
       "4        0.380260     0.393618     0.404868  自然受孕              1.0   \n",
       "5        0.377387     0.390282     0.399467  自然受孕              1.0   \n",
       "6        0.375591     0.387779     0.395050  自然受孕              1.0   \n",
       "...           ...          ...          ...   ...              ...   \n",
       "1053     0.376861     0.389914     0.397090  自然受孕              1.0   \n",
       "1054     0.377597     0.387901     0.404293  自然受孕              1.0   \n",
       "1055     0.379041     0.391748     0.400433  自然受孕              1.0   \n",
       "1056     0.379107     0.388544     0.401030  自然受孕              1.0   \n",
       "1057     0.379130     0.391535     0.401856  自然受孕              1.0   \n",
       "\n",
       "      IVF妊娠_encoded  \n",
       "2                 0  \n",
       "3                 0  \n",
       "4                 0  \n",
       "5                 0  \n",
       "6                 0  \n",
       "...             ...  \n",
       "1053              0  \n",
       "1054              0  \n",
       "1055              0  \n",
       "1056              0  \n",
       "1057              0  \n",
       "\n",
       "[990 rows x 14 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>孕妇代码</th>\n",
       "      <th>最早达到目标Y染色体浓度的孕周</th>\n",
       "      <th>年龄</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>孕妇BMI</th>\n",
       "      <th>Y染色体浓度</th>\n",
       "      <th>检测孕周</th>\n",
       "      <th>13号染色体的GC含量</th>\n",
       "      <th>18号染色体的GC含量</th>\n",
       "      <th>21号染色体的GC含量</th>\n",
       "      <th>IVF妊娠</th>\n",
       "      <th>ratio over 0.04</th>\n",
       "      <th>IVF妊娠_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A001</td>\n",
       "      <td>20.142857</td>\n",
       "      <td>31</td>\n",
       "      <td>160.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>28.515625</td>\n",
       "      <td>0.066171</td>\n",
       "      <td>20.142857</td>\n",
       "      <td>0.377449</td>\n",
       "      <td>0.390582</td>\n",
       "      <td>0.399480</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A001</td>\n",
       "      <td>20.142857</td>\n",
       "      <td>31</td>\n",
       "      <td>160.0</td>\n",
       "      <td>74.00</td>\n",
       "      <td>28.906250</td>\n",
       "      <td>0.061192</td>\n",
       "      <td>22.857143</td>\n",
       "      <td>0.375613</td>\n",
       "      <td>0.389251</td>\n",
       "      <td>0.397212</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A002</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>32</td>\n",
       "      <td>149.0</td>\n",
       "      <td>74.00</td>\n",
       "      <td>33.331832</td>\n",
       "      <td>0.059230</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>0.380260</td>\n",
       "      <td>0.393618</td>\n",
       "      <td>0.404868</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A002</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>32</td>\n",
       "      <td>149.0</td>\n",
       "      <td>76.00</td>\n",
       "      <td>34.232692</td>\n",
       "      <td>0.042401</td>\n",
       "      <td>16.714286</td>\n",
       "      <td>0.377387</td>\n",
       "      <td>0.390282</td>\n",
       "      <td>0.399467</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A002</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>32</td>\n",
       "      <td>149.0</td>\n",
       "      <td>75.00</td>\n",
       "      <td>33.782262</td>\n",
       "      <td>0.047825</td>\n",
       "      <td>19.714286</td>\n",
       "      <td>0.375591</td>\n",
       "      <td>0.387779</td>\n",
       "      <td>0.395050</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1053</th>\n",
       "      <td>A266</td>\n",
       "      <td>13.714286</td>\n",
       "      <td>30</td>\n",
       "      <td>159.0</td>\n",
       "      <td>83.35</td>\n",
       "      <td>32.969881</td>\n",
       "      <td>0.099052</td>\n",
       "      <td>17.714286</td>\n",
       "      <td>0.376861</td>\n",
       "      <td>0.389914</td>\n",
       "      <td>0.397090</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1054</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>73.76</td>\n",
       "      <td>30.703133</td>\n",
       "      <td>0.098706</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>0.377597</td>\n",
       "      <td>0.387901</td>\n",
       "      <td>0.404293</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>74.06</td>\n",
       "      <td>30.825814</td>\n",
       "      <td>0.102088</td>\n",
       "      <td>12.571429</td>\n",
       "      <td>0.379041</td>\n",
       "      <td>0.391748</td>\n",
       "      <td>0.400433</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>74.74</td>\n",
       "      <td>31.107551</td>\n",
       "      <td>0.109855</td>\n",
       "      <td>13.571429</td>\n",
       "      <td>0.379107</td>\n",
       "      <td>0.388544</td>\n",
       "      <td>0.401030</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1057</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>75.85</td>\n",
       "      <td>31.572341</td>\n",
       "      <td>0.122668</td>\n",
       "      <td>14.571429</td>\n",
       "      <td>0.379130</td>\n",
       "      <td>0.391535</td>\n",
       "      <td>0.401856</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>990 rows × 14 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 218
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T02:25:31.390732Z",
     "start_time": "2025-09-07T02:25:31.372375Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 计算每个孕妇的BMI标准差（验证波动幅度）\n",
    "processed_df['bmi_std'] = processed_df.groupby('孕妇代码')['孕妇BMI'].transform('std').fillna(0)\n",
    "processed_df['13_gc_std'] = processed_df.groupby('孕妇代码')['13号染色体的GC含量'].transform('std').fillna(0)\n",
    "processed_df['18_gc_std'] = processed_df.groupby('孕妇代码')['18号染色体的GC含量'].transform('std').fillna(0)\n",
    "processed_df['21_gc_std'] = processed_df.groupby('孕妇代码')['21号染色体的GC含量'].transform('std').fillna(0)\n",
    "processed_df"
   ],
   "id": "10a46615c3a4a796",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      孕妇代码  最早达到目标Y染色体浓度的孕周  年龄     身高     体重      孕妇BMI    Y染色体浓度       检测孕周  \\\n",
       "2     A001        20.142857  31  160.0  73.00  28.515625  0.066171  20.142857   \n",
       "3     A001        20.142857  31  160.0  74.00  28.906250  0.061192  22.857143   \n",
       "4     A002        13.857143  32  149.0  74.00  33.331832  0.059230  13.857143   \n",
       "5     A002        13.857143  32  149.0  76.00  34.232692  0.042401  16.714286   \n",
       "6     A002        13.857143  32  149.0  75.00  33.782262  0.047825  19.714286   \n",
       "...    ...              ...  ..    ...    ...        ...       ...        ...   \n",
       "1053  A266        13.714286  30  159.0  83.35  32.969881  0.099052  17.714286   \n",
       "1054  A267        11.571429  28  155.0  73.76  30.703133  0.098706  11.571429   \n",
       "1055  A267        11.571429  28  155.0  74.06  30.825814  0.102088  12.571429   \n",
       "1056  A267        11.571429  28  155.0  74.74  31.107551  0.109855  13.571429   \n",
       "1057  A267        11.571429  28  155.0  75.85  31.572341  0.122668  14.571429   \n",
       "\n",
       "      13号染色体的GC含量  18号染色体的GC含量  21号染色体的GC含量 IVF妊娠  ratio over 0.04  \\\n",
       "2        0.377449     0.390582     0.399480  自然受孕              0.5   \n",
       "3        0.375613     0.389251     0.397212  自然受孕              0.5   \n",
       "4        0.380260     0.393618     0.404868  自然受孕              1.0   \n",
       "5        0.377387     0.390282     0.399467  自然受孕              1.0   \n",
       "6        0.375591     0.387779     0.395050  自然受孕              1.0   \n",
       "...           ...          ...          ...   ...              ...   \n",
       "1053     0.376861     0.389914     0.397090  自然受孕              1.0   \n",
       "1054     0.377597     0.387901     0.404293  自然受孕              1.0   \n",
       "1055     0.379041     0.391748     0.400433  自然受孕              1.0   \n",
       "1056     0.379107     0.388544     0.401030  自然受孕              1.0   \n",
       "1057     0.379130     0.391535     0.401856  自然受孕              1.0   \n",
       "\n",
       "      IVF妊娠_encoded   bmi_std  13_gc_std  18_gc_std  21_gc_std  \n",
       "2                 0  0.276214   0.001298   0.000941   0.001604  \n",
       "3                 0  0.276214   0.001298   0.000941   0.001604  \n",
       "4                 0  0.402877   0.004447   0.004091   0.005707  \n",
       "5                 0  0.402877   0.004447   0.004091   0.005707  \n",
       "6                 0  0.402877   0.004447   0.004091   0.005707  \n",
       "...             ...       ...        ...        ...        ...  \n",
       "1053              0  0.363127   0.000995   0.002883   0.002983  \n",
       "1054              0  0.385880   0.000749   0.001993   0.001697  \n",
       "1055              0  0.385880   0.000749   0.001993   0.001697  \n",
       "1056              0  0.385880   0.000749   0.001993   0.001697  \n",
       "1057              0  0.385880   0.000749   0.001993   0.001697  \n",
       "\n",
       "[990 rows x 18 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>孕妇代码</th>\n",
       "      <th>最早达到目标Y染色体浓度的孕周</th>\n",
       "      <th>年龄</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>孕妇BMI</th>\n",
       "      <th>Y染色体浓度</th>\n",
       "      <th>检测孕周</th>\n",
       "      <th>13号染色体的GC含量</th>\n",
       "      <th>18号染色体的GC含量</th>\n",
       "      <th>21号染色体的GC含量</th>\n",
       "      <th>IVF妊娠</th>\n",
       "      <th>ratio over 0.04</th>\n",
       "      <th>IVF妊娠_encoded</th>\n",
       "      <th>bmi_std</th>\n",
       "      <th>13_gc_std</th>\n",
       "      <th>18_gc_std</th>\n",
       "      <th>21_gc_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A001</td>\n",
       "      <td>20.142857</td>\n",
       "      <td>31</td>\n",
       "      <td>160.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>28.515625</td>\n",
       "      <td>0.066171</td>\n",
       "      <td>20.142857</td>\n",
       "      <td>0.377449</td>\n",
       "      <td>0.390582</td>\n",
       "      <td>0.399480</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.276214</td>\n",
       "      <td>0.001298</td>\n",
       "      <td>0.000941</td>\n",
       "      <td>0.001604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A001</td>\n",
       "      <td>20.142857</td>\n",
       "      <td>31</td>\n",
       "      <td>160.0</td>\n",
       "      <td>74.00</td>\n",
       "      <td>28.906250</td>\n",
       "      <td>0.061192</td>\n",
       "      <td>22.857143</td>\n",
       "      <td>0.375613</td>\n",
       "      <td>0.389251</td>\n",
       "      <td>0.397212</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.276214</td>\n",
       "      <td>0.001298</td>\n",
       "      <td>0.000941</td>\n",
       "      <td>0.001604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A002</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>32</td>\n",
       "      <td>149.0</td>\n",
       "      <td>74.00</td>\n",
       "      <td>33.331832</td>\n",
       "      <td>0.059230</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>0.380260</td>\n",
       "      <td>0.393618</td>\n",
       "      <td>0.404868</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.402877</td>\n",
       "      <td>0.004447</td>\n",
       "      <td>0.004091</td>\n",
       "      <td>0.005707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A002</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>32</td>\n",
       "      <td>149.0</td>\n",
       "      <td>76.00</td>\n",
       "      <td>34.232692</td>\n",
       "      <td>0.042401</td>\n",
       "      <td>16.714286</td>\n",
       "      <td>0.377387</td>\n",
       "      <td>0.390282</td>\n",
       "      <td>0.399467</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.402877</td>\n",
       "      <td>0.004447</td>\n",
       "      <td>0.004091</td>\n",
       "      <td>0.005707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A002</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>32</td>\n",
       "      <td>149.0</td>\n",
       "      <td>75.00</td>\n",
       "      <td>33.782262</td>\n",
       "      <td>0.047825</td>\n",
       "      <td>19.714286</td>\n",
       "      <td>0.375591</td>\n",
       "      <td>0.387779</td>\n",
       "      <td>0.395050</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.402877</td>\n",
       "      <td>0.004447</td>\n",
       "      <td>0.004091</td>\n",
       "      <td>0.005707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1053</th>\n",
       "      <td>A266</td>\n",
       "      <td>13.714286</td>\n",
       "      <td>30</td>\n",
       "      <td>159.0</td>\n",
       "      <td>83.35</td>\n",
       "      <td>32.969881</td>\n",
       "      <td>0.099052</td>\n",
       "      <td>17.714286</td>\n",
       "      <td>0.376861</td>\n",
       "      <td>0.389914</td>\n",
       "      <td>0.397090</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.363127</td>\n",
       "      <td>0.000995</td>\n",
       "      <td>0.002883</td>\n",
       "      <td>0.002983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1054</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>73.76</td>\n",
       "      <td>30.703133</td>\n",
       "      <td>0.098706</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>0.377597</td>\n",
       "      <td>0.387901</td>\n",
       "      <td>0.404293</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.385880</td>\n",
       "      <td>0.000749</td>\n",
       "      <td>0.001993</td>\n",
       "      <td>0.001697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>74.06</td>\n",
       "      <td>30.825814</td>\n",
       "      <td>0.102088</td>\n",
       "      <td>12.571429</td>\n",
       "      <td>0.379041</td>\n",
       "      <td>0.391748</td>\n",
       "      <td>0.400433</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.385880</td>\n",
       "      <td>0.000749</td>\n",
       "      <td>0.001993</td>\n",
       "      <td>0.001697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>74.74</td>\n",
       "      <td>31.107551</td>\n",
       "      <td>0.109855</td>\n",
       "      <td>13.571429</td>\n",
       "      <td>0.379107</td>\n",
       "      <td>0.388544</td>\n",
       "      <td>0.401030</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.385880</td>\n",
       "      <td>0.000749</td>\n",
       "      <td>0.001993</td>\n",
       "      <td>0.001697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1057</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>75.85</td>\n",
       "      <td>31.572341</td>\n",
       "      <td>0.122668</td>\n",
       "      <td>14.571429</td>\n",
       "      <td>0.379130</td>\n",
       "      <td>0.391535</td>\n",
       "      <td>0.401856</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.385880</td>\n",
       "      <td>0.000749</td>\n",
       "      <td>0.001993</td>\n",
       "      <td>0.001697</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>990 rows × 18 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 219
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T02:25:31.663060Z",
     "start_time": "2025-09-07T02:25:31.619375Z"
    }
   },
   "cell_type": "code",
   "source": [
    "THRESHOLDS = {\n",
    "    'bmi_std': 1.0,\n",
    "    '13_gc_std': 0.006,\n",
    "    '18_gc_std': 0.006,\n",
    "    '21_gc_std': 0.006,\n",
    "}\n",
    "# 提前计算每组的均值和中位数\n",
    "grouped = processed_df.groupby('孕妇代码')\n",
    "\n",
    "# 计算每组的统计量\n",
    "aggs = grouped.agg(\n",
    "    bmi_mean=('孕妇BMI', 'mean'),\n",
    "    bmi_median=('孕妇BMI', 'median'),\n",
    "    gc13_mean=('13号染色体的GC含量', 'mean'),\n",
    "    gc13_median=('13号染色体的GC含量', 'median'),\n",
    "    gc18_mean=('18号染色体的GC含量', 'mean'),\n",
    "    gc18_median=('18号染色体的GC含量', 'median'),\n",
    "    gc21_mean=('21号染色体的GC含量', 'mean'),\n",
    "    gc21_median=('21号染色体的GC含量', 'median'),\n",
    ")\n",
    "\n",
    "# 获取之前计算的标准差（每个孕妇代码对应一个std）\n",
    "std_cols = ['bmi_std', '13_gc_std', '18_gc_std', '21_gc_std']\n",
    "std_values = grouped[std_cols].first()  # 每个孕妇的标准差（所有行相同）\n",
    "\n",
    "# 判断函数：根据标准差是否超过阈值，选择均值或中位数\n",
    "def select_value(mean, median, std, threshold):\n",
    "    return mean if std < threshold else median\n",
    "\n",
    "# 应用逻辑\n",
    "processed_df = processed_df.merge(aggs, on='孕妇代码', how='left')\n",
    "processed_df = processed_df.merge(std_values, on='孕妇代码', how='left', suffixes=('', '_tmp'))\n",
    "\n",
    "# 最终列：根据std选择\n",
    "processed_df['bmi_final'] = processed_df.apply(\n",
    "    lambda row: select_value(row['bmi_mean'], row['bmi_median'], row['bmi_std'], THRESHOLDS['bmi_std']),\n",
    "    axis=1\n",
    ")\n",
    "\n",
    "processed_df['13_gc_final'] = processed_df.apply(\n",
    "    lambda row: select_value(row['gc13_mean'], row['gc13_median'], row['13_gc_std'], THRESHOLDS['13_gc_std']),\n",
    "    axis=1\n",
    ")\n",
    "\n",
    "processed_df['18_gc_final'] = processed_df.apply(\n",
    "    lambda row: select_value(row['gc18_mean'], row['gc18_median'], row['18_gc_std'], THRESHOLDS['18_gc_std']),\n",
    "    axis=1\n",
    ")\n",
    "\n",
    "processed_df['21_gc_final'] = processed_df.apply(\n",
    "    lambda row: select_value(row['gc21_mean'], row['gc21_median'], row['21_gc_std'], THRESHOLDS['21_gc_std']),\n",
    "    axis=1\n",
    ")"
   ],
   "id": "8f525eb785ddeee5",
   "outputs": [],
   "execution_count": 220
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T02:25:31.725728Z",
     "start_time": "2025-09-07T02:25:31.711974Z"
    }
   },
   "cell_type": "code",
   "source": [
    "processed_df = processed_df.drop(columns=[\n",
    "    'gc21_mean', 'gc21_median', '18_gc_std',\n",
    "    'gc18_mean', 'gc18_median', '21_gc_std',  # 修复：你写成了'gc21_std'，应为'gc18_std'\n",
    "    'gc13_mean', 'gc13_median', '13_gc_std',  # 注意：列名一致性，确认是 '13_gc_std' 还是 'gc13_std'\n",
    "    'bmi_mean', 'bmi_median', 'bmi_std','bmi_std_tmp','13_gc_std_tmp','18_gc_std_tmp','21_gc_std_tmp'\n",
    "])\n",
    "processed_df"
   ],
   "id": "218076bd57e31020",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     孕妇代码  最早达到目标Y染色体浓度的孕周  年龄     身高     体重      孕妇BMI    Y染色体浓度       检测孕周  \\\n",
       "0    A001        20.142857  31  160.0  73.00  28.515625  0.066171  20.142857   \n",
       "1    A001        20.142857  31  160.0  74.00  28.906250  0.061192  22.857143   \n",
       "2    A002        13.857143  32  149.0  74.00  33.331832  0.059230  13.857143   \n",
       "3    A002        13.857143  32  149.0  76.00  34.232692  0.042401  16.714286   \n",
       "4    A002        13.857143  32  149.0  75.00  33.782262  0.047825  19.714286   \n",
       "..    ...              ...  ..    ...    ...        ...       ...        ...   \n",
       "985  A266        13.714286  30  159.0  83.35  32.969881  0.099052  17.714286   \n",
       "986  A267        11.571429  28  155.0  73.76  30.703133  0.098706  11.571429   \n",
       "987  A267        11.571429  28  155.0  74.06  30.825814  0.102088  12.571429   \n",
       "988  A267        11.571429  28  155.0  74.74  31.107551  0.109855  13.571429   \n",
       "989  A267        11.571429  28  155.0  75.85  31.572341  0.122668  14.571429   \n",
       "\n",
       "     13号染色体的GC含量  18号染色体的GC含量  21号染色体的GC含量 IVF妊娠  ratio over 0.04  \\\n",
       "0       0.377449     0.390582     0.399480  自然受孕              0.5   \n",
       "1       0.375613     0.389251     0.397212  自然受孕              0.5   \n",
       "2       0.380260     0.393618     0.404868  自然受孕              1.0   \n",
       "3       0.377387     0.390282     0.399467  自然受孕              1.0   \n",
       "4       0.375591     0.387779     0.395050  自然受孕              1.0   \n",
       "..           ...          ...          ...   ...              ...   \n",
       "985     0.376861     0.389914     0.397090  自然受孕              1.0   \n",
       "986     0.377597     0.387901     0.404293  自然受孕              1.0   \n",
       "987     0.379041     0.391748     0.400433  自然受孕              1.0   \n",
       "988     0.379107     0.388544     0.401030  自然受孕              1.0   \n",
       "989     0.379130     0.391535     0.401856  自然受孕              1.0   \n",
       "\n",
       "     IVF妊娠_encoded  bmi_final  13_gc_final  18_gc_final  21_gc_final  \n",
       "0                0  28.710938     0.376531     0.389917     0.398346  \n",
       "1                0  28.710938     0.376531     0.389917     0.398346  \n",
       "2                0  33.962434     0.379446     0.392051     0.401299  \n",
       "3                0  33.962434     0.379446     0.392051     0.401299  \n",
       "4                0  33.962434     0.379446     0.392051     0.401299  \n",
       "..             ...        ...          ...          ...          ...  \n",
       "985              0  32.459274     0.377345     0.390744     0.398945  \n",
       "986              0  31.052210     0.378719     0.389932     0.401903  \n",
       "987              0  31.052210     0.378719     0.389932     0.401903  \n",
       "988              0  31.052210     0.378719     0.389932     0.401903  \n",
       "989              0  31.052210     0.378719     0.389932     0.401903  \n",
       "\n",
       "[990 rows x 18 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>孕妇代码</th>\n",
       "      <th>最早达到目标Y染色体浓度的孕周</th>\n",
       "      <th>年龄</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>孕妇BMI</th>\n",
       "      <th>Y染色体浓度</th>\n",
       "      <th>检测孕周</th>\n",
       "      <th>13号染色体的GC含量</th>\n",
       "      <th>18号染色体的GC含量</th>\n",
       "      <th>21号染色体的GC含量</th>\n",
       "      <th>IVF妊娠</th>\n",
       "      <th>ratio over 0.04</th>\n",
       "      <th>IVF妊娠_encoded</th>\n",
       "      <th>bmi_final</th>\n",
       "      <th>13_gc_final</th>\n",
       "      <th>18_gc_final</th>\n",
       "      <th>21_gc_final</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A001</td>\n",
       "      <td>20.142857</td>\n",
       "      <td>31</td>\n",
       "      <td>160.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>28.515625</td>\n",
       "      <td>0.066171</td>\n",
       "      <td>20.142857</td>\n",
       "      <td>0.377449</td>\n",
       "      <td>0.390582</td>\n",
       "      <td>0.399480</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>28.710938</td>\n",
       "      <td>0.376531</td>\n",
       "      <td>0.389917</td>\n",
       "      <td>0.398346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A001</td>\n",
       "      <td>20.142857</td>\n",
       "      <td>31</td>\n",
       "      <td>160.0</td>\n",
       "      <td>74.00</td>\n",
       "      <td>28.906250</td>\n",
       "      <td>0.061192</td>\n",
       "      <td>22.857143</td>\n",
       "      <td>0.375613</td>\n",
       "      <td>0.389251</td>\n",
       "      <td>0.397212</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>28.710938</td>\n",
       "      <td>0.376531</td>\n",
       "      <td>0.389917</td>\n",
       "      <td>0.398346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A002</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>32</td>\n",
       "      <td>149.0</td>\n",
       "      <td>74.00</td>\n",
       "      <td>33.331832</td>\n",
       "      <td>0.059230</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>0.380260</td>\n",
       "      <td>0.393618</td>\n",
       "      <td>0.404868</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>33.962434</td>\n",
       "      <td>0.379446</td>\n",
       "      <td>0.392051</td>\n",
       "      <td>0.401299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A002</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>32</td>\n",
       "      <td>149.0</td>\n",
       "      <td>76.00</td>\n",
       "      <td>34.232692</td>\n",
       "      <td>0.042401</td>\n",
       "      <td>16.714286</td>\n",
       "      <td>0.377387</td>\n",
       "      <td>0.390282</td>\n",
       "      <td>0.399467</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>33.962434</td>\n",
       "      <td>0.379446</td>\n",
       "      <td>0.392051</td>\n",
       "      <td>0.401299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A002</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>32</td>\n",
       "      <td>149.0</td>\n",
       "      <td>75.00</td>\n",
       "      <td>33.782262</td>\n",
       "      <td>0.047825</td>\n",
       "      <td>19.714286</td>\n",
       "      <td>0.375591</td>\n",
       "      <td>0.387779</td>\n",
       "      <td>0.395050</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>33.962434</td>\n",
       "      <td>0.379446</td>\n",
       "      <td>0.392051</td>\n",
       "      <td>0.401299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>A266</td>\n",
       "      <td>13.714286</td>\n",
       "      <td>30</td>\n",
       "      <td>159.0</td>\n",
       "      <td>83.35</td>\n",
       "      <td>32.969881</td>\n",
       "      <td>0.099052</td>\n",
       "      <td>17.714286</td>\n",
       "      <td>0.376861</td>\n",
       "      <td>0.389914</td>\n",
       "      <td>0.397090</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.459274</td>\n",
       "      <td>0.377345</td>\n",
       "      <td>0.390744</td>\n",
       "      <td>0.398945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>73.76</td>\n",
       "      <td>30.703133</td>\n",
       "      <td>0.098706</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>0.377597</td>\n",
       "      <td>0.387901</td>\n",
       "      <td>0.404293</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.052210</td>\n",
       "      <td>0.378719</td>\n",
       "      <td>0.389932</td>\n",
       "      <td>0.401903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>74.06</td>\n",
       "      <td>30.825814</td>\n",
       "      <td>0.102088</td>\n",
       "      <td>12.571429</td>\n",
       "      <td>0.379041</td>\n",
       "      <td>0.391748</td>\n",
       "      <td>0.400433</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.052210</td>\n",
       "      <td>0.378719</td>\n",
       "      <td>0.389932</td>\n",
       "      <td>0.401903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>74.74</td>\n",
       "      <td>31.107551</td>\n",
       "      <td>0.109855</td>\n",
       "      <td>13.571429</td>\n",
       "      <td>0.379107</td>\n",
       "      <td>0.388544</td>\n",
       "      <td>0.401030</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.052210</td>\n",
       "      <td>0.378719</td>\n",
       "      <td>0.389932</td>\n",
       "      <td>0.401903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>75.85</td>\n",
       "      <td>31.572341</td>\n",
       "      <td>0.122668</td>\n",
       "      <td>14.571429</td>\n",
       "      <td>0.379130</td>\n",
       "      <td>0.391535</td>\n",
       "      <td>0.401856</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.052210</td>\n",
       "      <td>0.378719</td>\n",
       "      <td>0.389932</td>\n",
       "      <td>0.401903</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>990 rows × 18 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 221
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T02:25:31.838069Z",
     "start_time": "2025-09-07T02:25:31.826582Z"
    }
   },
   "cell_type": "code",
   "source": [
    "processed_df = processed_df.drop(columns=['13号染色体的GC含量','18号染色体的GC含量','21号染色体的GC含量','IVF妊娠','孕妇BMI','Y染色体浓度','检测孕周'\n",
    "])\n",
    "processed_df"
   ],
   "id": "f26f683e11a7f042",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     孕妇代码  最早达到目标Y染色体浓度的孕周  年龄     身高     体重  ratio over 0.04  IVF妊娠_encoded  \\\n",
       "0    A001        20.142857  31  160.0  73.00              0.5              0   \n",
       "1    A001        20.142857  31  160.0  74.00              0.5              0   \n",
       "2    A002        13.857143  32  149.0  74.00              1.0              0   \n",
       "3    A002        13.857143  32  149.0  76.00              1.0              0   \n",
       "4    A002        13.857143  32  149.0  75.00              1.0              0   \n",
       "..    ...              ...  ..    ...    ...              ...            ...   \n",
       "985  A266        13.714286  30  159.0  83.35              1.0              0   \n",
       "986  A267        11.571429  28  155.0  73.76              1.0              0   \n",
       "987  A267        11.571429  28  155.0  74.06              1.0              0   \n",
       "988  A267        11.571429  28  155.0  74.74              1.0              0   \n",
       "989  A267        11.571429  28  155.0  75.85              1.0              0   \n",
       "\n",
       "     bmi_final  13_gc_final  18_gc_final  21_gc_final  \n",
       "0    28.710938     0.376531     0.389917     0.398346  \n",
       "1    28.710938     0.376531     0.389917     0.398346  \n",
       "2    33.962434     0.379446     0.392051     0.401299  \n",
       "3    33.962434     0.379446     0.392051     0.401299  \n",
       "4    33.962434     0.379446     0.392051     0.401299  \n",
       "..         ...          ...          ...          ...  \n",
       "985  32.459274     0.377345     0.390744     0.398945  \n",
       "986  31.052210     0.378719     0.389932     0.401903  \n",
       "987  31.052210     0.378719     0.389932     0.401903  \n",
       "988  31.052210     0.378719     0.389932     0.401903  \n",
       "989  31.052210     0.378719     0.389932     0.401903  \n",
       "\n",
       "[990 rows x 11 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>孕妇代码</th>\n",
       "      <th>最早达到目标Y染色体浓度的孕周</th>\n",
       "      <th>年龄</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>ratio over 0.04</th>\n",
       "      <th>IVF妊娠_encoded</th>\n",
       "      <th>bmi_final</th>\n",
       "      <th>13_gc_final</th>\n",
       "      <th>18_gc_final</th>\n",
       "      <th>21_gc_final</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A001</td>\n",
       "      <td>20.142857</td>\n",
       "      <td>31</td>\n",
       "      <td>160.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>28.710938</td>\n",
       "      <td>0.376531</td>\n",
       "      <td>0.389917</td>\n",
       "      <td>0.398346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A001</td>\n",
       "      <td>20.142857</td>\n",
       "      <td>31</td>\n",
       "      <td>160.0</td>\n",
       "      <td>74.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>28.710938</td>\n",
       "      <td>0.376531</td>\n",
       "      <td>0.389917</td>\n",
       "      <td>0.398346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A002</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>32</td>\n",
       "      <td>149.0</td>\n",
       "      <td>74.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>33.962434</td>\n",
       "      <td>0.379446</td>\n",
       "      <td>0.392051</td>\n",
       "      <td>0.401299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A002</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>32</td>\n",
       "      <td>149.0</td>\n",
       "      <td>76.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>33.962434</td>\n",
       "      <td>0.379446</td>\n",
       "      <td>0.392051</td>\n",
       "      <td>0.401299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A002</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>32</td>\n",
       "      <td>149.0</td>\n",
       "      <td>75.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>33.962434</td>\n",
       "      <td>0.379446</td>\n",
       "      <td>0.392051</td>\n",
       "      <td>0.401299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>A266</td>\n",
       "      <td>13.714286</td>\n",
       "      <td>30</td>\n",
       "      <td>159.0</td>\n",
       "      <td>83.35</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.459274</td>\n",
       "      <td>0.377345</td>\n",
       "      <td>0.390744</td>\n",
       "      <td>0.398945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>73.76</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.052210</td>\n",
       "      <td>0.378719</td>\n",
       "      <td>0.389932</td>\n",
       "      <td>0.401903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>74.06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.052210</td>\n",
       "      <td>0.378719</td>\n",
       "      <td>0.389932</td>\n",
       "      <td>0.401903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>74.74</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.052210</td>\n",
       "      <td>0.378719</td>\n",
       "      <td>0.389932</td>\n",
       "      <td>0.401903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>75.85</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.052210</td>\n",
       "      <td>0.378719</td>\n",
       "      <td>0.389932</td>\n",
       "      <td>0.401903</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>990 rows × 11 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 222
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T02:25:32.080502Z",
     "start_time": "2025-09-07T02:25:32.069526Z"
    }
   },
   "cell_type": "code",
   "source": [
    "processed_df = processed_df.drop_duplicates(subset=['孕妇代码'], keep='first').reset_index(drop=True)\n",
    "processed_df"
   ],
   "id": "bed7c8f20706594a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     孕妇代码  最早达到目标Y染色体浓度的孕周  年龄     身高     体重  ratio over 0.04  IVF妊娠_encoded  \\\n",
       "0    A001        20.142857  31  160.0  73.00              0.5              0   \n",
       "1    A002        13.857143  32  149.0  74.00              1.0              0   \n",
       "2    A003        13.000000  35  160.0  78.70              1.0              0   \n",
       "3    A004        11.000000  26  158.0  71.50              1.0              0   \n",
       "4    A005        12.285714  30  150.0  67.40              1.0              0   \n",
       "..    ...              ...  ..    ...    ...              ...            ...   \n",
       "255  A263        12.571429  30  157.0  72.34              1.0              0   \n",
       "256  A264        11.714286  30  171.0  94.95              1.0              0   \n",
       "257  A265        11.714286  32  168.0  95.17              1.0              0   \n",
       "258  A266        13.714286  30  159.0  81.24              1.0              0   \n",
       "259  A267        11.571429  28  155.0  73.76              1.0              0   \n",
       "\n",
       "     bmi_final  13_gc_final  18_gc_final  21_gc_final  \n",
       "0    28.710938     0.376531     0.389917     0.398346  \n",
       "1    33.962434     0.379446     0.392051     0.401299  \n",
       "2    31.226562     0.380396     0.393254     0.402986  \n",
       "3    28.721359     0.380350     0.393046     0.403330  \n",
       "4    31.077778     0.376861     0.389487     0.398412  \n",
       "..         ...          ...          ...          ...  \n",
       "255  29.643738     0.379104     0.388323     0.399744  \n",
       "256  32.897314     0.379583     0.392359     0.404810  \n",
       "257  34.206054     0.377312     0.390401     0.403554  \n",
       "258  32.459274     0.377345     0.390744     0.398945  \n",
       "259  31.052210     0.378719     0.389932     0.401903  \n",
       "\n",
       "[260 rows x 11 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>孕妇代码</th>\n",
       "      <th>最早达到目标Y染色体浓度的孕周</th>\n",
       "      <th>年龄</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>ratio over 0.04</th>\n",
       "      <th>IVF妊娠_encoded</th>\n",
       "      <th>bmi_final</th>\n",
       "      <th>13_gc_final</th>\n",
       "      <th>18_gc_final</th>\n",
       "      <th>21_gc_final</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A001</td>\n",
       "      <td>20.142857</td>\n",
       "      <td>31</td>\n",
       "      <td>160.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>28.710938</td>\n",
       "      <td>0.376531</td>\n",
       "      <td>0.389917</td>\n",
       "      <td>0.398346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A002</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>32</td>\n",
       "      <td>149.0</td>\n",
       "      <td>74.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>33.962434</td>\n",
       "      <td>0.379446</td>\n",
       "      <td>0.392051</td>\n",
       "      <td>0.401299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A003</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>35</td>\n",
       "      <td>160.0</td>\n",
       "      <td>78.70</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.226562</td>\n",
       "      <td>0.380396</td>\n",
       "      <td>0.393254</td>\n",
       "      <td>0.402986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A004</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>26</td>\n",
       "      <td>158.0</td>\n",
       "      <td>71.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.721359</td>\n",
       "      <td>0.380350</td>\n",
       "      <td>0.393046</td>\n",
       "      <td>0.403330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A005</td>\n",
       "      <td>12.285714</td>\n",
       "      <td>30</td>\n",
       "      <td>150.0</td>\n",
       "      <td>67.40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.077778</td>\n",
       "      <td>0.376861</td>\n",
       "      <td>0.389487</td>\n",
       "      <td>0.398412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>A263</td>\n",
       "      <td>12.571429</td>\n",
       "      <td>30</td>\n",
       "      <td>157.0</td>\n",
       "      <td>72.34</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.643738</td>\n",
       "      <td>0.379104</td>\n",
       "      <td>0.388323</td>\n",
       "      <td>0.399744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>A264</td>\n",
       "      <td>11.714286</td>\n",
       "      <td>30</td>\n",
       "      <td>171.0</td>\n",
       "      <td>94.95</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.897314</td>\n",
       "      <td>0.379583</td>\n",
       "      <td>0.392359</td>\n",
       "      <td>0.404810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>A265</td>\n",
       "      <td>11.714286</td>\n",
       "      <td>32</td>\n",
       "      <td>168.0</td>\n",
       "      <td>95.17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.206054</td>\n",
       "      <td>0.377312</td>\n",
       "      <td>0.390401</td>\n",
       "      <td>0.403554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>A266</td>\n",
       "      <td>13.714286</td>\n",
       "      <td>30</td>\n",
       "      <td>159.0</td>\n",
       "      <td>81.24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.459274</td>\n",
       "      <td>0.377345</td>\n",
       "      <td>0.390744</td>\n",
       "      <td>0.398945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>73.76</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.052210</td>\n",
       "      <td>0.378719</td>\n",
       "      <td>0.389932</td>\n",
       "      <td>0.401903</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>260 rows × 11 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 223
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T02:25:32.212378Z",
     "start_time": "2025-09-07T02:25:32.197871Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 对身高体重和年龄进行归一化处理\n",
    "normalize_cols = ['年龄', '身高', '体重']\n",
    "# 对指定列进行归一化 (Min-Max Normalization)\n",
    "processed_df[normalize_cols] = (processed_df[normalize_cols] - processed_df[normalize_cols].min()) / (processed_df[normalize_cols].max() - processed_df[normalize_cols].min())\n",
    "processed_df"
   ],
   "id": "7d7a107ebc9cda3",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     孕妇代码  最早达到目标Y染色体浓度的孕周        年龄        身高        体重  ratio over 0.04  \\\n",
       "0    A001        20.142857  0.454545  0.516129  0.229885              0.5   \n",
       "1    A002        13.857143  0.500000  0.161290  0.241379              1.0   \n",
       "2    A003        13.000000  0.636364  0.516129  0.295402              1.0   \n",
       "3    A004        11.000000  0.227273  0.451613  0.212644              1.0   \n",
       "4    A005        12.285714  0.409091  0.193548  0.165517              1.0   \n",
       "..    ...              ...       ...       ...       ...              ...   \n",
       "255  A263        12.571429  0.409091  0.419355  0.222299              1.0   \n",
       "256  A264        11.714286  0.409091  0.870968  0.482184              1.0   \n",
       "257  A265        11.714286  0.500000  0.774194  0.484713              1.0   \n",
       "258  A266        13.714286  0.409091  0.483871  0.324598              1.0   \n",
       "259  A267        11.571429  0.318182  0.354839  0.238621              1.0   \n",
       "\n",
       "     IVF妊娠_encoded  bmi_final  13_gc_final  18_gc_final  21_gc_final  \n",
       "0                0  28.710938     0.376531     0.389917     0.398346  \n",
       "1                0  33.962434     0.379446     0.392051     0.401299  \n",
       "2                0  31.226562     0.380396     0.393254     0.402986  \n",
       "3                0  28.721359     0.380350     0.393046     0.403330  \n",
       "4                0  31.077778     0.376861     0.389487     0.398412  \n",
       "..             ...        ...          ...          ...          ...  \n",
       "255              0  29.643738     0.379104     0.388323     0.399744  \n",
       "256              0  32.897314     0.379583     0.392359     0.404810  \n",
       "257              0  34.206054     0.377312     0.390401     0.403554  \n",
       "258              0  32.459274     0.377345     0.390744     0.398945  \n",
       "259              0  31.052210     0.378719     0.389932     0.401903  \n",
       "\n",
       "[260 rows x 11 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>孕妇代码</th>\n",
       "      <th>最早达到目标Y染色体浓度的孕周</th>\n",
       "      <th>年龄</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>ratio over 0.04</th>\n",
       "      <th>IVF妊娠_encoded</th>\n",
       "      <th>bmi_final</th>\n",
       "      <th>13_gc_final</th>\n",
       "      <th>18_gc_final</th>\n",
       "      <th>21_gc_final</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A001</td>\n",
       "      <td>20.142857</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>0.229885</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>28.710938</td>\n",
       "      <td>0.376531</td>\n",
       "      <td>0.389917</td>\n",
       "      <td>0.398346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A002</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.161290</td>\n",
       "      <td>0.241379</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>33.962434</td>\n",
       "      <td>0.379446</td>\n",
       "      <td>0.392051</td>\n",
       "      <td>0.401299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A003</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>0.295402</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.226562</td>\n",
       "      <td>0.380396</td>\n",
       "      <td>0.393254</td>\n",
       "      <td>0.402986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A004</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.451613</td>\n",
       "      <td>0.212644</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.721359</td>\n",
       "      <td>0.380350</td>\n",
       "      <td>0.393046</td>\n",
       "      <td>0.403330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A005</td>\n",
       "      <td>12.285714</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.193548</td>\n",
       "      <td>0.165517</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.077778</td>\n",
       "      <td>0.376861</td>\n",
       "      <td>0.389487</td>\n",
       "      <td>0.398412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>A263</td>\n",
       "      <td>12.571429</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.419355</td>\n",
       "      <td>0.222299</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.643738</td>\n",
       "      <td>0.379104</td>\n",
       "      <td>0.388323</td>\n",
       "      <td>0.399744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>A264</td>\n",
       "      <td>11.714286</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.870968</td>\n",
       "      <td>0.482184</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.897314</td>\n",
       "      <td>0.379583</td>\n",
       "      <td>0.392359</td>\n",
       "      <td>0.404810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>A265</td>\n",
       "      <td>11.714286</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.774194</td>\n",
       "      <td>0.484713</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.206054</td>\n",
       "      <td>0.377312</td>\n",
       "      <td>0.390401</td>\n",
       "      <td>0.403554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>A266</td>\n",
       "      <td>13.714286</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.483871</td>\n",
       "      <td>0.324598</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.459274</td>\n",
       "      <td>0.377345</td>\n",
       "      <td>0.390744</td>\n",
       "      <td>0.398945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>0.318182</td>\n",
       "      <td>0.354839</td>\n",
       "      <td>0.238621</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.052210</td>\n",
       "      <td>0.378719</td>\n",
       "      <td>0.389932</td>\n",
       "      <td>0.401903</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>260 rows × 11 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 224
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T02:25:33.268813Z",
     "start_time": "2025-09-07T02:25:32.606821Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "# 确定最佳聚类数k大致范围\n",
    "features = processed_df[['年龄', '身高', '体重', 'bmi_final', '13_gc_final', '18_gc_final', '21_gc_final', 'ratio over 0.04','IVF妊娠_encoded']]\n",
    "scaler = StandardScaler()\n",
    "scaled_features = scaler.fit_transform(features)\n",
    "# 这里我们使用肘部法则来求解\n",
    "sse = []\n",
    "# 测试聚类\n",
    "k_range = range(2,50)\n",
    "for k in k_range:\n",
    "    md = KMeans(n_clusters=k).fit(scaled_features)\n",
    "    sse.append(md.inertia_)\n",
    "fig = plt.figure(figsize=(16,6))\n",
    "fig.add_subplot(121)\n",
    "plt.plot(k_range, sse, '*-')\n",
    "plt.xlabel('聚类数K')\n",
    "plt.ylabel('误差平方和（SSE）')\n",
    "plt.title('肘部法则确定最佳K值')\n",
    "plt.grid(alpha=0.3)\n",
    "\n",
    "# 利用轮廓系数法来求解\n",
    "sil_scores = []\n",
    "for k in k_range:\n",
    "    labels = KMeans(n_clusters=k).fit_predict(scaled_features)\n",
    "    sil_scores.append(silhouette_score(scaled_features, labels))\n",
    "fig.add_subplot(122)\n",
    "# 绘制轮廓系数图\n",
    "plt.plot(range(2, 50), sil_scores, 'ro-')\n",
    "plt.xlabel('聚类数K')\n",
    "plt.ylabel('轮廓系数')\n",
    "plt.title('轮廓系数确定最佳K值（越接近1越好）')\n",
    "plt.grid(alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "fef76677e6350323",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ],
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABjYAAAJOCAYAAAAUHj4bAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvc2/+5QAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3XucjPX7x/H3vTM7e7RYyyJnIYQoKVKKVEhF6quTUiTJL3RC5XzsW1HpoJRKSqmIVA71TQcqpZwPbZuzXeyy553Z2fn9MTs3Yw9mWTuz6/V8PDy+5r7vmb12fb497s9e93VdhsvlcgkAAAAAAAAAAKAMCPJ3AAAAAAAAAAAAAL4isQEAAAAAAAAAAMoMEhsAAAAAAAAAAKDMILEBAAAAAAAAAADKDBIbAAAAAAAAAACgzCCxAQAAAAAAAAAAygwSGwAAAAAAAAAAoMwgsQEAAAAAAAAAAMoMEhsAAAAAAAAAAKDMILEBAGXYhg0bSvwzXS6X0tPT5XQ6S+wzv//+e02dOtXn67ds2aKnn35aCQkJxf5aq1ev1rffflvs952pNWvWKD4+Pt/x+Ph4HTt2rES+xuuvv66FCxcqNzfXPJaUlKTHHntM3333XYl8DQAAgLJux44dWr16tVwuV75zL730knbu3Fmsz/vrr780Z86ckgrPlJaWJrvdXuj5rVu3Fni/HxcXd9pfk/vsgpXWfXZ6enqh/+aJiYlasWJFgecKWssn27ZtmxITEws898knn+j3338v8v05OTmn/BqSe/0tW7bMp2sL4nQ69eWXXyo1NTXfOV++T0maOnWqfv3119P6+snJyaf1PgCBx+rvAADgXJObm6vMzMx8x8PCwhQU5J1vfuutt7RkyRItXrw43/VxcXG64447NH36dHXr1q1YMRw9elTZ2dmKjY3Nd+7IkSPq0KGDZs+erauuuqrA93/++eeF3nRfccUVuuOOO7yO7dy5U0uXLtVTTz0lyX0ze+zYMdlsNhmGIcl9Ix0aGqqQkBC98cYb2r9/v6pWrVqs70uS/ve//+nLL7/UsmXLVKVKFZ/eM2TIEJ82RXPmzJHNZivw3JQpU5STk6PFixcrODjYPH7bbbdp2LBh+X4mxWW32zVnzhy1bdtWPXr0kM1mU1BQkDIyMrRkyRJdd9115rVOp1NOp7PQWAEAAMqzr776SitWrNCVV17pdTwuLk6zZs1SamqqRo8ene99TqezwF/uHj58WM8//7yuv/56xcTEmMdzcnKUk5OjihUrmsdcLpd5PCcnR+np6UpJSVFKSooSEhK0Z88e7dq1S5s2bdLff/+t2267TePGjTPf//333+vDDz/Uyy+/rA8//FCpqal68cUX1adPHz355JNq06aNevXqpccff1x33XVXgd8D99nFU1r32ceOHdMDDzyg7t2769577813ftu2bRo3bpyuvfbafOcefvhh3XjjjQoPD1fjxo1Vo0aNfNc89thjuvPOO9W3b9985z799FOdd955uvjiiwuMLSEhQf369dMTTzyha665RpL75zJr1iylpKToyJEj2rt3r3bt2qW0tDRJUtWqVdW2bdvi/Agkudfj8OHD9dVXX6lChQpe51599VVlZWWpV69eSkxMVLt27fK9Pzs7W++++65atGhhHtuzZ4+ysrLy7acl9z67Zs2aktz//7znnnvUs2dPDRgwoNixAwgsJDYAoJTFx8cXmIhYunSpGjVq5HXMYrGYGxKP7du3KygoSEFBQerSpYvmzJmjJk2ayOl0KjQ0VDt37tTgwYO93hMSEqK1a9cqPDxckvTCCy/om2++0YQJE9S1a1evaz036UXdrO/Zs0e///67HnnkEa/jc+fONW+yv/zyS+3fv18DBgxQaGio1+cdOHBAnTt3zve5L7zwgipXrqyvv/5aYWFhuvzyy82fw/vvv6+GDRt6XZ+enp7vMx566CHt3r1b+/fvV2hoaL7zERER+Y5t2bJFQ4YMUceOHQv8ftevX6+hQ4cW+jNZs2aNtm/frlmzZik9PV0Oh0MWi8WM3W63KykpSS6XSw6HQ9HR0bLZbPrll190zz33aNGiRWratKkkafbs2XrhhRf0xhtveCWWVq9erZSUFA0aNEidO3fW4cOHvWIYMmSI1+v+/fvrySefLDBeAACA8iYrK0tJSUmy2Wxas2aNunbtqqSkJPMJ/JiYGH344YeKjIzUkiVLdP/996t69epenzF//nxNmjRJkZGR+T4/PDxcN910k9cxp9OpmJgYrwd+pk2bpnfeeUeS+z4wNDRUOTk5qlChglq3bq1q1aqpXr16uuSSSxQTE6OIiAg5HA7zF/bt2rXTK6+8orfffls2m03BwcH6/vvvlZiYqJYtWyouLk5ZWVm6+uqrC/w5cJ8dmPfZ2dnZuvfee9W8efMCE1KSFBwcLKu14F/TZWVlKS0tTUlJSXr22Wc1e/ZsNWnSxOsam82mkJAQSdJ3332nmTNnmj/XPXv2aOvWrbrtttskSQ6HQ1deeaWGDRsmSYqNjVXbtm01bNgwvf/++2rZsqUkdyXLddddp0aNGunyyy9XtWrVFBsbq2rVqqly5cr54kxPT1efPn00ZsyYApMSnjg93+/JLBaLDhw4oCNHjuixxx7TE088oRtvvNHrmn/++UcWi8Vrnb/yyiv66aefvBIbLpdLiYmJuvTSS/X+++9LkgzD0EsvvaQBAwbIarXqvvvuKzBGAGUDiQ0AKGWeG7hVq1apVq1aSk9PV5s2bQq8sTMMI99TJwMHDlRKSop53DAM9erVSy6XS+3bt1e/fv0kSStXrlRoaKhGjBihyMhIM6khSU8//bQk6ZFHHtHEiRPVtWtXuVwuBQcHKzs7W5L7CZ309HS5XC5lZ2crKirKjNFqtSoiIkJ33XWXsrKyzI3Nl19+ad6orl+/Xps2bSrwSZjzzjtPGzZs0MaNG3X33XdrzZo1Cg8PV1JSkm699VYNHDhQI0aMkCQNHjxYFSpUyLfZyszMVJs2bQr9Of/www8FHl+/fr3Xz0Jy30BPnDixwH8Dyf1UUUFP/3i8+OKLqlu3rjp37qzJkyfrvffe8zo/ZcoUTZkyxXy9YMECXXTRRfk+Z9OmTXrppZfUr1+/fNUys2fPVosWLdSyZUt99tlnCg0NldVq1YYNG3Tvvfdq8eLFql27tiT3z6awTREAAEB59Ouvv2rgwIGyWCzKycnR5s2bNXv2bOXk5Kht27Z65pln9PHHH2v06NGKj4/XI488onfffdfrvjA4OFiVKlXS2rVrJbkrrUeMGKHOnTurR48ePsXx6KOPauDAgYqMjJTNZtNvv/2mwYMHq1WrVpoyZYoqVKigPXv2KCEhQZdcckm+9//www/q1q2bKleurPXr1ys9PV1xcXHq3LmzfvnlF8XHx6tRo0Y677zzzPdkZ2fLarXKYrFwnx2g99mTJk1SjRo1NHHixHzn5s+fr82bNyspKUnHjh3T6NGjZbVavSp5QkJCZLFY1LdvX1WtWtVMKjgcDv3111+KioqS3W7XoUOHtHXrVl1wwQUaPny4DMOQy+UyY16/fr3OO+88RUdH56u6GTt2rHbv3q3x48dr4cKFZuXKAw88YCY6iuJwOPTkk08W2irtl19+0aJFixQcHCzDMPTiiy/KarVq+PDhZpLR87O/+OKLNXv2bGVkZJjvt9vtslgs2rhxo5o1a+aVXJs2bZrX17Lb7Xr88cf166+/6v/+7/+8ztWvX19z5szRbbfdpjZt2qhVq1an/N4ABCZ+6wEApaywm3rPzZ3nKRtJ+vPPP3Xo0CHNmjVL1apVU58+ffT9999r4sSJuvTSS81qi4kTJ2rbtm167bXXtHHjRklSdHS0goODtXHjRo0ZM8bra9lsNo0fP14tWrRQt27dNHz4cP3vf//zumbgwIFerwvbJAwaNEgpKSn67LPP8n2fRX2vISEh+uOPP9SsWTNVqlRJdrtdQ4YMUZMmTcwnh7Zt26bVq1fr66+/zvcZngTK66+/XugTaydavXq1+WTOyZxOp6ZPn64uXboU+N5ffvnFTBid7IsvvtBff/2lSy+9VIZh6JFHHtHQoUPNG/ZrrrlGQ4YMUa9evZSVlaXMzExFR0fn+5yMjAyNGDFCjRs3NjebJ8b+119/qX///pLk1ULMk4iqVq2a+ZRcQU/LAQAAlGcdO3bU1q1b9dFHH+mDDz7Q0qVLzXPHjh3THXfcoVatWum2226Tw+HQ3XffrXvuuUcvvPCC6tSpI0nm/dvHH3+sH374QRaLRT///LM2b96sr7/+WvXr11fXrl01Y8YMr6/9+OOP64ILLpDk/sWsw+HQ77//rsWLF+vff//Vm2++qYsuukiJiYmaOXOmPvroI9WvX1/Tpk1Ts2bNvD7ryy+/1JEjR3TgwAHt2bNH1apV008//SSn06mgoCDt2rVLO3fuzPe0/gcffKBLLrmE++wAvM/esWOHVqxYUeDPWpIaN26sihUr6t9//9Wff/6pyy+/XIZhaNmyZRo5cqRq1KihI0eO6M8//9Trr7+unJwcXXzxxXruueeUmpqqfv36KSQkRFlZWZo1a5Zefvllbdq0SbGxserbt686duxoVp28/fbbWrt2rVnBcCKLxaLp06d77eFyc3ML3dOdKDs7W4MGDSpwboZHtWrV1L59e1ksFn3yySdq06aNIiIilJGRoSZNmqh+/fpKT09XWlqa1q9fbya9PBVRU6dO1QcffGB+nuf/A/Xr1/f62e7Zs0fDhg2T3W7Xp59+arahOlHdunX1xBNPaPLkyVqwYMEpvz8AgYnEBgCUMk9rqYJKxIODg70SGxaLRUFBQbLZbF4bhczMTI0fP17t27dXeHi4Vq5cqTvuuEOGYXjdeP7666/Kycnx2kjs3btXhw4dUuvWrdWnTx9J7iGKnqe8PBUk7733ntq1a6fc3FxlZ2cXWh5us9nylfH7asWKFWbPWpvNpkcffVTr1q1Thw4dzL62knT77bfL5XLJYrGYT4g5nU7dc889qlKliuLi4rz6CJ/I5XLJbrcrODhY99xzT4ED6WJjY/Xf//5X//3vfwuNtV69erLb7V4/h+TkZE2bNs3sq5ybm6ucnBxFRER4/TtarVaFhIQoJCREYWFhyszMzPfznDRpkg4dOqTZs2d7nbPb7Zo8ebLXtXa7XS1atFBkZKSsVqsqVaqkG264QZJ7435yeT0AAEB557kP/OSTT9SrVy/99ttvSklJUaNGjfTwww8rOTlZb775pgzDkM1m0xtvvKEBAwaoZ8+eGjhwoB588EHzF+YXX3yxatWqJZvNZrYNysnJUXh4uNLS0rRjxw7NmTNHP/74oxYuXJgvyfDHH3+YDwlZLBZzBkRubq4qVKigZ599VrfeemuBlQozZszQ33//rXvuuUf16tVTs2bNFBMTo//7v/+T3W5Xx44d9eqrr+qyyy7TkSNHdO2112rZsmVmRYEH99nH+fs+e/78+brlllu8ZrF45Obm6qKLLpLVatW6dev06aefqkePHrLb7frhhx/UsmVLvf/++3r++ecVHBysoUOH6ueffzarfaKjo7V582ZJUq9evXTXXXepV69eSktLk8Vi0X/+8x+tWrVKDodDycnJWrNmjT788EPl5OTIbrcrPDxcDofDrN4/MbHjcDgkSU8++WS+1mO5ubmy2+0aOnSounTpokOHDqlSpUp65ZVXCq32Oe+881S/fn1J7nkgnTp1UvXq1c21+PXXX+uHH37QG2+8oXnz5mn9+vWqV6+e+f7hw4fr0UcfVY8ePTRs2DB169ZNEydOzDfD5b///a/sdrs+/PDDIhNRPXv21PPPP68tW7bkSzACKBtIbACAn3z55ZeqUaOGMjMz1aFDB1kslnz9Wy0WixISEsx2TqmpqTp27Jj69OmjH374QX/99ZdZsty5c2ft2rXLvAGVpOXLl+uqq67y6hP8wQcfaO7cubrvvvs0bNiwfMmUkwUFBSksLKzI8ycPffPFhg0btGHDBsXGxurZZ5+VJI0cOVI7duzQVVddpalTp2r06NGqXr26HnnkEf3yyy966KGHzPfbbDaNHj1a69atU58+fWS1Ws3Nod1uN1+7XC45nU716dNH48ePN9/vcrmUlZWlkJAQffjhhz7F7HQ6lZGRodDQULlcLj3xxBPKycnRrbfeqo0bNyoxMbHAjc7TTz9ttv+S3Df13377rfl6+fLlWrhwocaOHau6det6vXfmzJnavXu3V7sBz+by1Vdfzde7tl27dkX+ewIAAJRXP/zwg/7++2/dcssteu2117R7924zqdGxY0dNnTpVL730kiSpUqVKeu+99zR27Fj9+++/5gNFQUFBatiwoR544AFlZ2d7DeAeMGCALrroIgUHB6tx48Z6/vnndffdd+f7pX+7du00d+5c1atXTzabTU899ZRWr16tq666ShMmTNDevXs1ZMgQTZ06VVFRUeb7XC6XvvjiC02aNEmDBw/Wvn37lJSUpEOHDumhhx7SlVdeqZycHLlcLkVERGjXrl0yDEO1a9f2+oU999mBdZ+9YcOGQmdybNmyRf/5z38UFhamoKAgpaSk6OKLL1Z2drZXe6Xq1atr8+bN2rdvnx599FGtXLmy0AfP7Ha73nvvPc2cOdM8duGFF5p/9zzcdsstt2jq1KlatGiR18/wjz/+UEREhDkk/PbbbzfbVo0aNUo33XST2rZtK7vdbs6IrFmzpl588cUifw7t27dXbm6u2eLqxhtvlNPp1NSpU72+z4MHD0pyJz+mTJmiSy+9VJIUGRmp3bt3KzExUR07dlRISIh27NiR74HBqlWryuFwnLK6Jjg4WJdffrn++usvEhtAGUViAwBKmWeAYWhoaL6brfT0dC1atEh33nlnge9dsWKFRo4cab72lExLMgeSv/DCC+axjRs3ej3lIrmfuKlTp44mTZqkY8eOaejQocrOzjafUMvMzJQkJSUlmTeVDodDTqcz32d5FPcGPzc3VxMmTJAkderUSenp6Zo8ebKeeuqpIt9XUBn0xRdfrC1btphDBCX3puPJJ59Ur169zGNOp9Prffv27TNvgk+cZeJyueRyuQp8gs7zGcuXL1etWrWUkZGh0aNHa+/evZLcN9E///yzebMuuX+OoaGhZr/h3Nxcr+STJL3xxhuSZD5tdeLPaf369XrwwQe1fft2r3NBQUG69957822knU5ngU/UAQAAlGfp6emaOHGibrnlFlWuXFlhYWGy2Wxq0qSJlixZosWLF2v58uVe79m+fbumTZumnJwcSTITG5K7Qvruu+9WrVq1JLmfuvfcx0vue8k//vjD697bIzQ0VJdffrm2bNmiESNGKDExURMnTjR/oXzw4EGtWbNGAwYM0Ntvv23uCRITE/XJJ59o8uTJateund588001b95cd9xxhxYuXKjZs2crKipK27ZtU5cuXZSYmKjY2FivX3Bznx1499n79+/3qoQ40YUXXqhNmzaZrzt06KB33nlHjRs31nfffWcer127tpYtW6avvvpKN998s1dC7ERff/215s+fr3fffVd33XWXLBaLvv76ax05ckT/+c9/zH/LE6trrr/+erVv315bt27Vww8/bK4FzxD1nj17mg+yjRkzxmxnfKKi5qR4rFu3zvz7sGHDdPHFF+uuu+7yGlRfu3ZtHTx4UOvWrVOFChXMpIbHTz/9pKZNmyomJkZZWVnavHmzHnroIW3atEk2m83sQJCRkWHO+rDb7apZs2aBFTOxsbE6cODAKWMHEJhIbABAKfPcbB8+fFhWq9VMJEjufqCTJk3S+eefn+8JIUm66aab1LNnzwL717pcLjkcDv3999/mseeff159+/bVzJkzvYam9e3bV40bN1bDhg01ZswYffvtt+bTSVlZWQoLC9MzzzzjFXNoaKh++eUXSfLa1KWmppolxSefK8zUqVPNOG+++WYlJydr8uTJstlscjqd+vLLL/Xdd98pIyNDQUFBmjdvnnJycgr8vg3D0GeffaZrr71WlSpVKvRrnrghk9xPc23cuFFLly7VF198oblz50qSPvzwQ61Zs0bPPfec2arAw1Nu7/lZvfbaa4qKitKrr75qfo2Th/B169ZNffr0yVeNc6LatWvr8ssv1yeffKIHH3zQbCUQFBSk119/XZGRkQW+f+7cuQU+SQYAAHCucblcOnDggD766CN99NFHZkVBy5Yt9fTTT8tisZj3g2lpaZoyZYo+/fRTvf766+rUqZMkKSsry7zffPTRR5WTk2PODLjxxhvVsmVL2e12Se6Wr9WrV8/31HxmZqbWr1+vJUuWaMmSJerQoYPefvtts1I7MzPTbC01ZcoUDRkyRG+88YZsNpuqVaumV199VcHBwVq6dKk++ugjffXVV7JarerQoYNWrVqlq666yqxI+Pfff3X++ed7fX3us70Fwn12cHCw1y/vT7Zhwwbt3bs3X7LAIzk5WfXr19fmzZt14MABM1njsWPHDi1dulTx8fHau3evHnroIWVnZ6tixYpmYiMlJSXfDMWsrCyzPVqFChXMX/B7Ehvx8fGKjIw8rer8ghw7dkyffPJJoTNVnE6n0tPTVbNmTY0ZM8Zs4XaiZcuW6ZprrpEkrV27VjabTVFRUbrtttvMGY/Z2dnKzc0126xlZ2fr+eefN1uznSgtLU1Vq1Ytke8PQOkjsQEApczzRNjtt9+e79wFF1ygG2+8US+++KI++uijfOc9ramuvPLKfOeCgoK0detWr6eIGjRooKlTp2rw4MG67rrrzKGGkvsJrIyMDK8SZbvdrmuvvVaXX365V0nwyTwbOknatWuXV/nvyU9JFeS6667ThRdeqMcffzzfuezsbHXv3r3AEvmhQ4fmu/7QoUN6+umnlZSUpAcffNDruOcpHU85fOPGjc0N1ImbqXXr1pkbFU/v4nHjxumzzz4zn+zJyMjQTTfdpIkTJ5pfo6Anpex2u1eJdWhoqPl1PBs2zzwTj6efflrNmjXTokWL9Oqrr2rKlClFfg2P//u//8u3mU5JSSn0egAAgPIqMjJSCxcuVExMjCpVqqRZs2Zp586dmjlzplwul+bPny+Hw6EPPvhAr732mqKjo/XCCy943Venp6crNDRU69ev14svvqgKFSp4/cJ/wIABZjujm266SV9++aUmTpyocePGmdfs2bNH9913n6Tj9+c33nijMjMz5XQ6FR4eroiICIWHh6tZs2Zau3atHn/8cb344otKS0tT27Ztvb6v9u3bm39ftWqVDMPQf//7XyUnJ+uPP/5Q69atva7nPjvw7rPr1aunHTt2qHnz5gWe37Rpk5YvX24mNgYOHKjGjRurb9++2r9/v2699VZNnDhRVapUUY0aNdSoUSPl5uYqKChIR44cUa9evVS7dm2FhYXpscceU69evXTNNddo3759Xl/n5FkwkjtR0LBhQ69jnv3kmjVrzFZTHrm5uQXOUvHF4cOHNWPGDLPrwKxZs/TWW29p4cKFktzdCNq0aaOLL75YX3/9tW666Sbz+5Tc//6edXfw4EEtWbJEnTt3Vps2bbRlyxbz60ybNk27du0yk2JF2blzZ76qEABlB4kNAChlnl6la9asUXR0tDms2+OBBx7Q448/bl53Ms/m6oMPPlDTpk3Nzxo2bFiB11999dWqVauWli5d6pXYcDgc6tevn+666y7ddNNNkqTPP/9cubm5Zqn6tm3b1LBhw3yl6RdccIFyc3P177//6tChQ2rZsqUkqUePHuaTVA6Ho9Akx8UXX6w6deoUeC4jI0Nr1qwxy8JDQkK0adMmHT16tMDrFy9eLJvNpttuu83r+Ouvv663335bknujk5mZqZUrVxZYBn7JJZfke5Js3LhxWrVqlRYtWqQaNWqoV69euv766wuM4UTz58/32jBJ7sqZ559/3ny9YMECXXTRRebrmJgYValSRX379tW7776rQYMG5esBXJCZM2dSsQEAAJCncePG+Y7Z7XbzF9IbNmxQ5cqVNW3aNHXo0CHftUlJSapWrZpatmyp7777TqGhoVq+fLlmzZql1q1bq127dmar1qCgIE2cOFHXX3+9evfubd4PN27cWPPmzVOVKlVUqVIlhYWFKSQkRLNmzdLvv/9u3nN6fPLJJ2a7pgoVKmjt2rXat2+f7rrrLi1ZskTVqlXTm2++qR9++MFsi9WyZUvNmTNHP/zwg+69916vz+M+O/Duszt16qSFCxfqlltuKfB8cnKyVzXK66+/rnr16umtt95SQkKCHn/8cdWoUUOJiYmyWq06cuSInnjiCc2aNUtVqlTRm2++qUsvvdRsdSa593VWq1VTpkzRgQMHzNkyQ4cOVaNGjfTII4+YibbCYvriiy+8Wh9L7sSGLxX6hX1m5cqVzcTJ4MGD1bt3b7NNWI0aNXTffffp9ttvl9Pp1IEDB/Taa69pyJAhatCggWw2mxYvXqzRo0ebycIPPvjgtGKRpLi4OG3cuNEreQigbCGxAQCl7NChQ7JYLIWWczdq1EifffZZoX1KPYmNkJAQsx+vzWbLVz6enZ2tgwcPauPGjUpISMjX+/aFF17Q0aNHzWqLw4cP66WXXtLMmTNVqVIlORwOjRgxQi1atMhXvdGtWzd169ZN06ZNU2xsrBo1aqSPP/7YazaI3W73quwojGcT6JGYmKiuXbvqySef1LPPPqvY2Fg9/PDD+u233/Too496XZuVlaX58+erYsWK+b7WM88849X7tyh//vmnuZlKTU3VxRdfrODgYPXu3Vvz5s3TDTfcoNTUVJ9uem+66SZ16dJFFStWlM1mU8+ePdW7d2/169dPDodDWVlZhT4dNmDAAH300Ud65ZVX9Nxzz53yaxXW+xcAAOBcs3v3bs2ZM0dHjx5VcnKy/vnnHx09elQ33HCDbrjhBtWoUUMXX3yxXnvttUI/4++//1adOnW0d+9eJSQkaM+ePXr11VdVsWJFHT16VJ9++qmZENm2bZt27Nghm82mrVu3et3TtmnTRqNGjVLHjh3Vo0ePfF9n586dmjBhgsaMGeP1y2jDMFS5cmWNGTNGVapU0S+//KJmzZpp/vz5mjRpknndfffdpyFDhuiCCy7wekDqZNxnH+fP++zevXvrjTfe0PLly9W1a9d85//55x999dVXZvWQpxqle/fuuuyyy1S7dm3df//9evbZZ/XJJ5/oxhtvVHR0tEJDQyVJl19+eb7PrFixonbt2qVFixapR48eOnz4sI4ePao//vhDkydPLnKwttPpNIed9+3b1+vcmVRsxMXFKTExUePHjzfnlYSHh6tVq1aaPn26brjhBj3xxBO66KKLdNVVV+mee+5RWlqa1+D1qlWr6vXXX1ePHj0UFxenP/74Q61atSp2LC6XS9OmTdNNN92k6Ojo0/p+APjfqaf7AABK1N9//61atWrlS1yceONc1PC1k3vYFnY8OztbvXr10pNPPqmYmBivHqXLly/X3LlzNWXKFEVGRsput+uxxx7Tgw8+qEsuuUSSu7fqm2++qV9++aXAMt6//vpL77//vu6//35t2LBB48eP14gRI5SdnS1JGj9+vD777LNT/DTcT859//33ktxtujZs2KAWLVrIarWaT69ZrdYCfyazZs1SYmKiOnTooD59+mjNmjWn/HoFadasmWbOnKmRI0eqY8eO+uuvv/Tqq6/qoYce0sqVKzV8+HANGTKk0H+XE59cqly5smrVqqUKFSooJCREhmHIYrEoJCREkZGRiomJyVfW7hEdHa077rhDX375pVlmXZS5c+dqy5YtXn+K6n8MAABQXlWtWlURERHq2LGjHnnkEV133XW6+uqr9e233xbYlkly33t+8MEHyszMVG5urjZs2KC6devqueee0/jx47VmzRpZLBbVqlVLffv29bqf3rt3r0aPHq1rr71WPXv2NI+7XC49/fTTWrFiRaGDpiMiIpSTk6PevXsXeL88evRoDRkyRD///LNuueUWJSUl6ffff1diYqIkmfeSYWFhXvP6TsZ99nH+vM+uVKmSRo0apVGjRmnHjh35zm/evFlPP/20pk6dqqSkJGVlZUmS6tevr0suuUQTJ07Uddddpz59+mjWrFlq06aNV9uuwtStW1dvv/22MjIy1L17dw0aNEjXX399oYPMd+3aJUn67rvvdO211+qpp57K90v/oqryT2Xz5s26//77tWnTJq1YscJcuzabTTfddJMWLFigo0ePavz48Xrsscd0ww03aPTo0fninT59urKysjRu3Dg9//zzPiWqTvbSSy9p27ZtGjFixGl9LwACAxUbAFDKfvjhB6/yaIfDIcMwzB6zp+K5sb/11lu9jnvKiD3no6KiNH78eFWuXFmXX3652U5q3bp1euKJJ/Sf//xHl1xyiVJSUnTfffdp+/btCgoK0tKlS5WamqrU1FSlpaUpJydHr7zyiho0aGA+bbV8+XKNHDlSF110ke68805ZrVbNnTtXgwcPVr9+/fTee+95bSoK2nCd+MRTSEiIrrvuOq1YsUKHDx/W1VdfXeT1krRo0SLNmTNHQ4cO1aBBg/TZZ59p8ODBuvDCC5WVlaWdO3dq06ZNCgsLk2EYstvtSk1NlWEYZvLmkUce0e+//64qVarorbfeUpMmTeRyudSoUSMNHDhQ//zzj0JDQ1W/fn29/fbbSklJ0VVXXZWvvD8nJ0cOh0NpaWlKSkpScHCwuZF1Op1KTU012xY4nU5lZ2ebgwtPdv/992v+/PmaNWuWXnjhBfO4ZxOxZ88eM4mVlJRkfq6Hy+XSkSNHFB8fr/POO6/QzR0AAEB5EhYWpieeeMJ8/eOPP3rdJxmGoQMHDigpKcl8Wv2HH37Q+PHjVb16daWlpengwYNq166d7rvvPhmGoeTkZA0YMEBNmjQxn4qPj4+X0+nUlVdeqV9//VVhYWHm1zh69KiefPJJbd682attrOS+X/TcH9asWVPvv/++JkyYoJEjRyo+Pt7rF6yxsbFq1qyZPv74Y1188cUaOHCgPv74Y/MJ/FdffVWjR4/W3Llzdf/99+uVV17x+gU099mBd5998803a/fu3brjjjv02muvmbNUtm3bpqSkJN1+++3q3LmzpkyZonvuuUft27dX27Zt1aBBA/Xv319RUVH6999/5XQ69dBDD+no0aPKyMgw94Aul0tHjx7NlyBq0KCBmjZtqh9//FHNmjXT0qVLtXPnTj388MPmWrDb7XrhhRf0+eef65577tE777yjxMREXXHFFfroo49UrVo1xcTEKDQ0VG+99ZbCwsK0fv16HT16VBUqVDD/zYtit9u1atUqvfnmm3r00Uc1a9Ysvfbaa/r222/VoUMHNWrUSC1bttQll1yivXv3yuVy6bbbblNKSor27dun8847T+np6Zo4caL+97//af78+apfv74qV66sZ599Vrfffru5dpKSkvJ1MzjRxIkTtWTJEs2dO5dqDaCMI7EBAKVo8+bNWrdunVlyPnv2bP3xxx968MEHFRkZaV535MgR7d69Wz/++KPOO+88r8/wDB8/ecaGp3z8xI2JZ3bGidauXavo6Ghz8xQVFaXGjRurXbt2qlWrlmJjY1WlShVVqVJFlStXVmRkpJ577jl99NFHuv7667Vx40Y98cQTatGihV577TXzpvGSSy7Re++9p99//928yT98+LAefPBB/fvvvwX2sm3VqpUMw1BUVJSefPJJ9e7dW/3791eFChW8rlu3bp2ef/558/iePXs0ZcoUdevWzRxk2KtXL3Xq1Emff/65bDabvvrqK33wwQdmBYnHs88+a958jxkzRlFRUV6bktdee01HjhzRt99+q9dee00TJkxQy5YttWfPHs2fP18vv/yyJk+e7DV3JDU1VceOHdPatWs1fPjwfE++zZs3T/PmzZPk3jjZ7XatWLFC7dq10/bt273ii46O1vr16/P9rDytvf7v//5P8fHxCg8P1zPPPJPvOsk9JNHhcOjLL78stMcyAABAeZaTk+N1X3zJJZfotdde82rbY7Va1aNHD3Xu3Fnbtm1Tr169dOGFF0py31PfeOONCgoKUseOHc33ZGdnKzs7u8Bfaq9fv14HDhzQhx9+aP5yffHixVq+fLnWrVunTp06mddaLBaNHTtWlStXNttJ5ebmat68efrmm28UFxenAQMGqF+/frJarQoJCdG4ceOUmpqq119/XZdddpk6duyo/v37q1u3bnr//fe9Bj1znx1499lDhw5VjRo1vH6ZHhcXp1tuuUUWi0WxsbGaMWOG9u/fr+XLl+v333/Xxx9/rOTkZKWnp8vpdJqtoOrXr6+vv/5akru6ZPbs2UpKSlLjxo3lcrk0fvx4/fbbb/rnn3904YUX6uWXX1aHDh20f/9+TZ8+XYMGDdJdd92lZ555RuvWrdPSpUs1d+5cc3+5bds2rVixQt9++6327NmjpKQk86G3E40ZMyZfYuPkn7sk7du3Tw0aNFCzZs0kScOGDdMDDzygVatWac2aNVq5cqUOHz6s1NRU5eTkmJU6hmFo9erVys3N1f3336/ExETNmzdP9evXlyRdd911uuyyy1SxYkW9//77mjdvnnbt2qXhw4cX+u/QqFEjLViwQPXq1SvWvx+AwGO4Trc5HgCg2Ox2u0aOHKlx48Z5JTJOdujQIXXt2lWNGzfW+PHj1aRJE/PckSNH1KdPH7322mvm8V9//VWjRo3SypUr9euvv+ruu+/WH3/8UWjv1LS0tCK//smysrJktVrNJEZcXJzq1KmTb6h4QUaNGqXw8HD17t3b66m1gvzyyy9q06aN+bmefr5HjhzRrFmzdNddd5kD0BMSElSlSpUin8aR3Jtazx+Xy6Xw8PBC23mdzOVyFdpCAAAAAIFr0qRJ+ueffzRnzpzT/oykpCSvYce+OPn+cePGjXr++efVsmVL3XnnnYW2AfL4+uuvlZqaqu7du3sNdj58+LAWLlyoe+65x+t4SkqKvv/+e914441Ffi732YErJyfnlD/rouzbt09LlixRhw4d1KJFC0nSkiVLlJKSog4dOhT4C/zvv/9eF154oTm0PDMz06v6qDAOh8NMGhb33/xMv8+4uDhFRUWpatWqBZ7fsWOH5s+fr8suu0xdu3Ytsr0zgPKBxAYAAAAAAAAAACgzSF8CAAAAAAAAAIAyg8QGAAAAAAAAAAAoM0hsAAAAAAAAAACAMoPEBgAAAAAAAAAAKDNIbAAAAAAAAAAAgDKDxAYAAAAAAAAAACgzrP4OINAcOZIql+v46+BgixwOp/8CQpnBWkFxsF7gK9YKioP1Al+V1FoxDKlKlQolEFH5xf4Cp4u1guJgvcBXrBUUB+sFvvLH/oLExklcLnltPDzHAF+wVlAcrBf4irWC4mC9wFesldLB/gJngrWC4mC9wFesFRQH6wW+Ku21QisqAAAAAAAAAABQZpDYAAAAAAAAAAAAZQaJDQAAAAAAAAAAUGaQ2AAAAAAAAAAAAGUGiQ0AAAAAAAAAAFBmkNgAAAAAAAAAAABlBokNAAAAAAAAAABQZpDYAAAAAAAAAAAAZQaJDQAAAAAAAAAAUGaQ2AAAAAAAAAAAAGUGiQ0AAAAA5cKOHTvUu3dvtW3bVtOmTZPL5TrlexYsWKArrrhCzZs3V//+/ZWYmGiee+utt9S+fXu1adNGjzzyiJKTk89m+AAAAAB85NfExsqVK9W5c2c1a9ZMffr0UVxcnKSiNySnew4AAABA+WW32zVo0CA1b95cn376qeLi4vTZZ58V+Z5169Zp5syZmj59ulatWqXs7GxNmzZNkvTbb79p0aJFmjdvnj7//HOvcwAAAAD8y2+Jjd27d2vUqFEaMWKEVq9erZo1a2r06NFFbkhO9xwAAACA8m316tVKS0vTyJEjVadOHQ0fPlwLFy4s8j3x8fEaO3as2rdvr+rVq6tXr17atGmTJGnDhg268sor1aBBA9WtW1fdu3fXv//+WwrfCQAAAIBT8VtiIy4uTsOGDVO3bt0UExOjvn37atOmTUVuSE73HAAAAIDybdu2bWrVqpXCwsIkSU2aNDErwgvTp08fde3a1XwdHx+vunXrSpIaNWqkFStWaPfu3Tpy5IgWLlyo9u3bn71vAAAAAIDPrP76wldffbXXa88moqgNyemeAwAAAFC+paWlqVatWuZrwzAUFBSkY8eOqWLFiqd8f3JyshYsWKDnnntOknTllVeqbt26uvbaayVJLVq00MCBA89O8AAAAACKxW+JjRPZ7Xa9/fbbuvfee7Vnz55CNyRFbVbOdCMDAAAAoOyyWCyy2Wxex0JCQpSVleXTfmDcuHFq3bq1OnXqJElatmyZ9u/fr6+++krR0dGaNm2aHn/8cb388svFiis42OL12mq1FHJlgHA6Zfn5JwUlHFRubHU523eQLAEeczkV8GsFAYX1Al+xVlAcrBf4yh9rJSASGzNmzFB4eLhuu+02zZgxo9ANSVGblTPdyHj4c+Ox+UCKXvwuTsOubqjmNaJK7euiZPAfexQH6wW+Yq2gOFgv8FV5XCsVK1bUzp07vY6lp6crODj4lO9duHCh1q1bp0WLFpnHvvzyS/Xt21cNGjSQJI0ePVoXX3yxUlJSFBXl+726w+GUy+V9zG53+vz+0mRb+oUin35Clv37zWPOmjWVNnG67D16+jGyc1egrhUEJtYLfMVaQXGwXuCrklgrhuH7tX5PbPz000/66KOP9PHHHys4OLjIDcnpnisOf248Fv91QL/tOqrFfx1QoyoRpfI1UbL4jz2Kg/UCX7FWUBysF/iqtDceZ1uLFi28Zuzt3btXdrv9lA85bdiwQZMnT9brr7+umJgY87jT6dThw4fN14mJiebx8si29AtF3X+3Tt4MBR04oKj771bKnPdJbgAAACBg+G14uCTt2bNHjz32mMaOHavzzz9fkntD8tdff5nXnLghOd1zgexASpa2JqRqW0Kqlm87JElavv2QtiWkamtCqg6kZPk5QgAAACDwtW3bVqmpqWbVxezZs9W+fXtZLBalpaXJ4XDke8/hw4c1aNAgDRgwQM2bN1d6errS09MlSW3atNHHH3+sDz/8UJ9//rmGDx+u1q1bq3LlyqX5bZUOp1ORTz8huVw6OVdl5CU6Ip9+UiqnSR0AAACUPX6r2MjKytKDDz6oLl26qHPnzuYG4pJLLjE3JDfffLPXhuTEzUpxzgWynm/+mu9YcoZDd89bb77+bcSVpRkSAAAAUOZYrVZNmDBBI0aM0PTp0+V0OjVv3jxJUs+ePTVq1Ch16dLF6z1Lly7VkSNHNGPGDM2YMcM8vn37dvXr10+JiYl69dVXlZycrNatW2vSpEml+S2VmuC1P3u1nzqZ4XLJsn+fgtf+LEeHjqUYGQAAAFAww+U6ufFS6Vi5cqUefvjhfMdXrVqlbdu2acSIEYqIiDA3JI0aNTLfdzrnfHX4cKpX9bXNZjmrLR2+2pqgcV/vkDM3/z+DJcjQmOsb64amsWft66PknO21gvKF9QJfsVZQHKwX+Kqk1ophSDExFUogopKTkJCgjRs3qk2bNoqOjvZ3OKW+vzgdIZ99oqhB95/yupTX5yi7V59SiAhSYK4VBC7WC3zFWkFxsF7gK3/sL/yW2DiVojYkp3vOF/7YeGxLSPWq0PB4/67WuiA2sDaKKBz/sUdxsF7gK9YKioP1Al+V58RGoCkLiY3gn35QpVu6n/K6o59/ScVGKQrEtYLAxXqBr1grKA7WC3zlj/2F34eHFyY2NlaxsQVXKpzuOQAAAACAN8dl7eWsWVNBBw6YMzVO5DIM5daoKcdl7f0QHQAAAJCfX4eHw61yuE1VwoMVE2GTJFUMtapKeLAqh9v8HBkAAACAcs9iUdrE6ZKkk9MaLsM9Tjxt4jQpwOcXAgAA4NxBYiMAxFYI0RcD2mn0te55IBVDrfpiQDvFVgjxc2QAAAAAzgX2Hj2VMud9uaKivI7n1qiplDnvy96jp58iAwAAAPIL2FZU5xqbNUgX1nRvInYfzVKmwymblbwTAAAAgNJh79FTmdu2KGL6ZEnS0Y8XydHxKio1AAAAEHD4zXkAqRQWrDqVwyRJmw+m+jkaAAAAAOcaIzPT/HtO6zYkNQAAABCQSGwEmAtruKe+bzqQ4udIAAAAAJxrjIz043/PyvJjJAAAAEDhSGwEmAtruNtRbTxAxQYAAACA0mWkH09s6ITqDQAAACCQkNgIMC3yKjY2H0hVrsvl52gAAAAAnFMyMsy/UrEBAACAQEViI8CcHxOhEGuQUrNztDuJJ6QAAAAAlB6vVlSZGUVcCQAAAPgPiY0AY7UEqVl1d9XGBuZsAAAAAChFJ7aiomIDAAAAgYrERgBqwQBxAAAAAH5gnNCKihkbAAAACFQkNgKQZ4D4JgaIAwAAAChFXq2oqNgAAABAgCKxEYAuzKvYiDucrnR7jp+jAQAAAHCu8G5FRcUGAAAAAhOJjQBUNTJE1SuEKNclbT2Y5u9wAAAAAJwjvIeHk9gAAABAYCKxEaA87ag2MmcDAAAAQCk5sWJDVGwAAAAgQJHYCFAtanoGiDNnAwAAAEApcDhkOBzmSyOTGRsAAAAITCQ2AtTxAeIpcrlcfo4GAAAAQHl3YhsqiRkbAAAACFwkNgLUBdUiFWwxlJTh0L5jPCkFAAAA4OzyakMlychiHwIAAIDARGIjQNmsQWpSLVIS7agAAAAAnH1GRob3gcyMgi8EAAAA/IzERgA7sR0VAAAAAJxNRnqa92sqNgAAABCgSGwEsBY13APEN1KxAQAAAOAsO7liw8hkxgYAAAACE4mNAOap2NiemKYsh9PP0QAAAAAoz/IPD6diAwAAAIGJxEYAqxEVoujwYDlzXdqemHbqNwAAAADA6TppeLiyqNgAAABAYCKxEcAMw1ALc84G7agAAAAAnD20ogIAAEBZQWIjwLWoyQBxAAAAAGefkVex4bLZ3K+p2AAAAECAIrER4C7MGyC+YT+JDQAAAABnjyexkRtdxf06kxkbAAAACEwkNgJcs+oVFGRIiWl2JaRm+zscAAAAAOWUZ3i4q0qM+wAVGwAAAAhQJDYCXFiwRefHREiSNtOOCgAAAMBZYlZs5CU2jCwqNgAAABCYSGyUAZ45GxsZIA4AAADgLPEMD8+tEu1+TcUGAAAAAhSJjTLAM2eDAeIAAAAAzhZPKyqzYiOTxAYAAAACE4mNMuDCGu6Kja0Jacpx5vo5GgAAAADlkacVlcscHp4puVz+DAkAAAAoEImNMqBu5TBFhVqVnZOrnYfT/R0OAAAAgHLIbEWVl9iQJGVn+ykaAAAAoHAkNsoAwzDMdlQb99OOCgAAAEDJM9LTJEm5MTHHjzFnAwAAAAGIxEYZ4WlHxQBxAAAAAGeDp2LDFVVRLovFfSwry58hAQAAAAUisVFGtGCAOAAAAICzyDM83BURIVdomPtgXrIDAAAACCQkNsqI5tXdFRt7j2YpOcPu52gAAAAAlDfm8PDwCCks1H2Mig0AAAAEIBIbZUSFUKvqR4dLkjbRjgoAAABACTNbUZ1QscGMDQAAAAQiEhtlyIW0owIAAABwNuTmSpl5iY3wCLnCPIkNKjYAAAAQeEhslCEX1mSAOAAAAICzIDNThsslSXKFhx+fsUHFBgAAAAIQiY0ypGUNd2Jjy8FUOXNdfo4GAAAAQHlhnDgkPDxcCs2bsZFBYgMAAACBh8RGGVK/SrjCgy1KtzsVfyTj1G8AAAAAAB8Y6WmS3NUaCgpixgYAAAACGomNMsQSZKhZ3pyNjczZAAAAAFBCzMHh4RHu/w3Lq9hgxgYAAAACEImNMqYFA8QBAAAAlDAjI13SiYmNcPcJKjYAAAAQgEhslDEX1mCAOAAAAICSZaTnJTYi8hIanhkbmVRsAAAAIPCQ2ChjLsyr2Ig/kqHUrBw/RwMAAACgPMjXisozYyOT2X4AAAAIPCQ2ypjocJvOq+h+emrLQao2AAAAAJy548PDPYkNZmwAAAAgcJHYKINa1PS0o2LOBgAAAOCxY8cO9e7dW23bttW0adPkcrlO+Z4FCxboiiuuUPPmzdW/f38lJibmu2bYsGGaMGHC2Qg5YJgVGxGeGRvuig1mbAAAACAQ+T2xkZycrGuuuUZ79+41jy1atEidOnVS69atde+993qdK2qzcjobmbLo+ABxKjYAAAAASbLb7Ro0aJCaN2+uTz/9VHFxcfrss8+KfM+6des0c+ZMTZ8+XatWrVJ2dramTZvmdc0PP/ygtWvX6v/+7//OZvh+d3x4eN6MjbzEBhUbAAAACER+TWwkJSVp0KBB2rdvn3ls9+7dmjFjhmbNmqUvv/xSNWvW1MiRIyUVvVk5nY1MWeUZIL7pQEq5Td4AAAAAxbF69WqlpaVp5MiRqlOnjoYPH66FCxcW+Z74+HiNHTtW7du3V/Xq1dWrVy9t2rTJPJ+VlaVx48ZpxIgRioqKOtvfgl8dHx5+8owNKjYAAAAQePya2Bg+fLi6devmdWzLli1q1aqVmjdvrpo1a6pXr16Kj4+XVPRm5XQ2MmVVo6oRCrEG6VhWjnYns9EAAAAAtm3bplatWiksr9KgSZMmiouLK/I9ffr0UdeuXc3X8fHxqlu3rvn61VdfVVZWlqxWq9asWVOuHyrKPzycGRsAAAAIXH5NbEyYMEH9+vXzOnb++edr7dq12rJli1JTUzV//nx16NBBUtGbldPZyJRVwZYgXVAtUhLtqAAAAABJSktLU61atczXhmEoKChIx44d8+n9ycnJWrBgge644w5J0v79+/XOO++obt262r9/v5577jkNGTKk3CY3zOHhEe5WVJ4ZG0Zmht9iAgAAAApj9ecXr127dr5j559/vq677jrdcsstkqRatWrpk08+kVT0ZqWocxUrVjzL30npu7BGlP7an6KNB1LUvXmsv8MBAAAA/Mpischms3kdCwkJUVZWlk/7gXHjxql169bq1KmTJOmzzz5TTEyM3nnnHdlsNt1zzz265ppr9NNPP+mKK67wOa7gYIvXa6vVUsiV/mXJGxJuqRApm80ia6Q7wRGUnS2bLTBjLu8Cda0gMLFe4CvWCoqD9QJf+WOt+DWxUZA///xT3333nT755BM1bNhQs2fP1oABA7Rw4cIiNytnupHxKCsbj9Z1KuqD3/dq88FUNhoBIlDXCgIT6wW+Yq2gOFgv8FV5XCsVK1bUzp07vY6lp6crODj4lO9duHCh1q1bp0WLFpnHEhISdNlll5l7jMjISNWtW1d79+4tVlwOh1MnF3nY7c5ifUZpCElzz9hwhIa74wt2t6JyZWYEZLznCn72KA7WC3zFWkFxsF7gq5JYK4bh+7UBl9hYtmyZunfvrpYtW0qSHn30UX300Ufatm1bkZuVM9nInKisbDwuqOpuRbUzMU3H0u0KCy5/m9OyKBDXCgIX6wW+Yq2gOFgv8FVpbzzOthYtWnjN2Nu7d6/sdvspH3LasGGDJk+erNdff10xMTHm8erVq3u1ts3NzdXBgwdVs2bNkg8+AJitqE6esZHJjA0AAAAEHr/O2CiI0+nU4cOHzdfp6enKyMiQ0+lUixYt9Ndff5nnTtysFHWuPIqtEKJqkTY5XdLWBOZsAAAA4NzWtm1bpaammlUXs2fPVvv27WWxWJSWliaHw5HvPYcPH9agQYM0YMAANW/eXOnp6UpPd1cu3HDDDfruu+/0zTff6ODBg3r++edlt9vVpk2b0vy2So05PDzCk9hwz9hQXosqAAAAIJAEXGKjTZs2WrFihebOnaslS5Zo8ODBiomJUZMmTYrcrBR1rrxqUTNKkrRpP4kNAAAAnNusVqsmTJigMWPGqH379vrmm280YsQISVLPnj31/fff53vP0qVLdeTIEc2YMUNt2rQx/0hSgwYN9OKLL+q1115T165d9f333+vVV19VZGRkqX5fpcWT2FC4e7aGwjwVGyQ2AAAAEHgCrhVVt27dFB8fr3fffVeHDh1So0aN9PLLL5stpSZMmKARI0Zo+vTpcjqdmjdvnqTjG5mCzpVXF9aI0qodh7XxQIq/QwEAAAD8rkuXLlq+fLk2btyoNm3aKDo6WpL07bffFnj9vffeq3vvvbfQz+vUqZM5TLy8y9+Kyl2xYVCxAQAAgAAUEImN7du3m383DENDhgzRkCFDCry2sM3Kqc6VRy1qVJAkbTyQKpfLJSOQmhwDAAAAfhAbG6vY2Fh/h1Hm5G9FlVexkcWMDQAAAASegEhsFFdRm5VzaSPTpFqkLEGGjqTbdf+Hf+qxa85Xs+oV/B0WAAAAgDLGyJstYlZshLlbUhlZWZLLFViT4gEAAHDOC7gZG/BdaLBFjau6Nx4bD6Rq2ZYEP0cEAAAAoCwyMvISG3kVG54ZG5IkqjYAAAAQYEhslFEHUrK0NSFVtSoe33As335I2xJStTUhVQdS2HwAAAAA8IHdLsPhkCS58oaHe2ZsSJKRmeGXsAAAAIDClMlWVJB6vvlrvmPJGQ7dPW+9+fq3EVeWZkgAAAAAyiBPtYZ0vBWVrFa5rFYZOTkysrLk8lNsAAAAQEGo2CijxndrIktQwX1uLUGGxndrUsoRAQAAACiLzMHhwcGSzWYe91RtGFmZfokLAAAAKAyJjTLqhqaxmnvHRQWem3vHRbqh6bkxQB0AAADAmTl5cLgpLK8dVSZtbgEAABBYSGwAAAAAwDks3+DwPK4wKjYAAAAQmEhslGGVw22qEh6supXdGw6LIVUJD1blcNsp3gkAAAAAbmYrqrzB4R6u0FD3+UwSGwAAAAgsDA8vw2IrhOiLAe1kyKVrX1urdLtTU3s2U2yFEH+HBgAAAKCMMNLTJOVvRcWMDQAAAAQqKjbKOJs1SMFWiy6tW1mS9Nvuo/4NCAAAAEDZ4qnYOKkVlfIqNpixAQAAgEBDYqOcaF/PndhYE5/k50gAAAAAlCWFtqJixgYAAAACFImNcuLy+tGSpE0HUnU0w+HnaAAAAACUFZ5WVDq5FZWZ2KBiAwAAAIGFxEY5EVshRI2qRsglae2uZH+HAwAAAKCMMNILbkVlDg+nYgMAAAABhsRGOXJ5PXfVxs+0owIAAADgIyPDMzzcuxWV8oaHK5PEBgAAAAILiY1ypH39vDkb/yYr1+XyczQAAAAAyoLjFRuRXsfNig0SGwAAAAgwJDbKkVY1oxRhs+hopkNbD6b6OxwAAAAAZUDhw8Pdr5mxAQAAgEBDYqMcsVqC1K6uu2rj53jmbAAAAAA4Nc/wcFe+4eHM2AAAAEBgIrFRznjaUf38L3M2AAAAAJyaWbFx0vBwc8YGFRsAAAAIMCQ2yhnPAPHNB1KVnGH3czQAAAAAAp2Rni6pgFZU5oyNjFKPCQAAACgKiY1yplqFEDWqGiGXpLW7aEcFAAAAoGhGRl5iI9/wcHfFhpFJxQYAAAACC4mNcqh9fXfVBnM2AAAAAJxK4cPD8xIbzNgAAABAgCGxUQ555mys/TdZzlyXn6MBAAAAEMgKbUUVxowNAAAABCYSG+VQyxpRirBZdDTToa0Jqf4OBwAAAEAAK6wVlTwzNqjYAAAAQIAhsVEOWS1Buqyeu2rj5/gkP0cDAAAAIJAV2orKnLFBYgMAAACBhcRGOdW+HnM2AAAAAJxCbq4Pw8NJbAAAACCwkNgopy7Pm7Ox5WCqkjPsfo4GAAAAQEA6IWlxcsWGwtytqJixAQAAgEBDYqOcqhoZokZVI+SStOZfqjYAAAAA5OcZHC5J8gwLz+MKcyc6DBIbAAAACDAkNsqxDvU97aiYswEAAAAgP7MNVXiEFOS9PXQxPBwAAAABisRGOdY+L7Gx9t9kOXNdfo4GAAAAQKApbHC4dMKMjexsyeks1bgAAACAopDYKMda1IxSZIhFx7JytOVgqr/DAQAAABBgjPQ0SZIrIiLfOU/FhiTmbAAAACCgkNgox6xBhtrVdQ8Rpx0VAAAAgJMdr9jIn9g4ceYGczYAAAAQSEhslHOedlQ/M0AcAAAAwEk8w8MLakUli0Uum819HXM2AAAAEEBIbJRz7eu5Kza2HExVUobdz9EAAAAACCTm8PCIyALPm3M2SGwAAAAggJDYKOdiIkPUuKq7rHwtVRsAAAAATlDU8HDphDkbmbSiAgAAQOAgsXEO6NAgrx0VczYAAAAAnKDIVlSS5KnYyMworZAAAACAUyKxcQ5oX8+d2Fj7b7KcuS4/RwMAAAAgUJyyFVWYu2KD4eEAAAAIJCQ2zgEX1oxShRCrjmXlaPPBVH+HAwAAACBAnLIVVRgzNgAAABB4SGycA6xBhtrVrSSJdlQAAAAAjjPS0yRJroiIAs97hoeLig0AAAAEEBIb54j29ZmzAQAAAMDb8YqNghMbyhsebmRSsQEAAIDAQWLjHHF5XmJja0KajqTb/RwNAAAAgEBgDg+PKKQVlTk8nMQGAAAAAgeJjXNETIRNTaq5BwKu/TfZz9EAAAAACAg+Dw8nsQEAAIDAQWLjHNK+fmVJtKMCAAAA4Hbq4eF5x5mxAQAAgABCYuMc0iGvHdXaXcnKyXX5ORoAAAAA/uZpRaVChoczYwMAAACBiMTGOaR5jShVCLEqJStHmw+k+DscAAAAAH5meFpRFTI83JyxQcUGAAAAAgiJjXOINchQu7p57aiYswEAAACc88zh4YW1ojIrNjJKLSYAAADgVEhsnGM6NHAnNtYwZwMAAAA455kzNgodHu6u2GDGBgAAAAIJiY1zzOX13HM2tiak6XC63c/RAAAAACVnx44d6t27t9q2batp06bJ5Tr1XLkFCxboiiuuUPPmzdW/f38lJibmu8bhcOjGG2/UL7/8cjbC9qvjragKrtgwZ2xkMWMDAAAAgcPviY3k5GRdc8012rt3b75z//3vfzVo0CCvY0VtVk5nI3OuqRJh0wXV3E9jrf2Xqg0AAACUD3a7XYMGDVLz5s316aefKi4uTp999lmR71m3bp1mzpyp6dOna9WqVcrOzta0adPyXffWW29px44dZyt0/7HbZTgckiRXIcPDXWHuhIeRScUGAAAAAodfExtJSUkaNGiQ9u3bl+/cjh07NH/+fI0aNco8VtRm5XQ2Mueq9vXz5mzEM2cDAAAA5cPq1auVlpamkSNHqk6dOho+fLgWLlxY5Hvi4+M1duxYtW/fXtWrV1evXr20adMmr2v+/fdfvf322zrvvPPOZvh+4anWkIoaHk7FBgAAAAKPXxMbw4cPV7du3fIdd7lcevbZZ9WvXz/VqVPHPF7UZuV0NjLnqvb13e2ofvonSYMW/KUtB1P9HBEAAABwZrZt26ZWrVopLG8mRJMmTRQXF1fke/r06aOuXbuar+Pj41W3bl2va5599lkNGDCgfCY2PIPDg4Ol4OACr/HM2DAySWwAAAAgcPg1sTFhwgT169cv3/GPP/5Y27ZtU61atfTdd9/JkVceXdRm5XQ2MueqC2tEKSrUqgyHU7/vPaZlWxL8HRIAAABwRtLS0lSrVi3ztWEYCgoK0rFjx3x6f3JyshYsWKA77rjDPPbpp58qLS1N/fv3L/F4A8HxweEFV2tIMmdsMDwcAAAAgcTqzy9eu3btfMfS09M1Y8YM1a1bVwcPHtTixYv1+uuv67333itys1LUuYoVK5bK91MWHEjJ0tFMh5rGRuqXXUclScu3H1KP5rFySaoUFqwaUaF+jREAAAAoLovFIpvN5nUsJCREWVlZPu0Hxo0bp9atW6tTp06S3G1zX3jhBb311luyWk9/2xQcbPF6bbVaCrmy9AU58pIVERGy2QqOyxLpTnoEZWcVeg3OjkBaKwh8rBf4irWC4mC9wFf+WCt+TWwUZMWKFcrMzNS7776rSpUq6cEHH9SNN96oRYsWFblZOdONjEcgbzxKQs83f813LDnDobvnrTdf/zXy6tIMqdwob2sFZxfrBb5iraA4WC/wVXlcKxUrVtTOnTu9jqWnpyu4kBZLJ1q4cKHWrVunRYsWmccmTZqkW2+9VU2bNj2juBwOp1wu72N2u/OMPrOkBB9NkSTlhoUXGpM1OMT9l4zMgIn7XMLPHMXBeoGvWCsoDtYLfFUSa8UwfL824BIbBw8eVMuWLVWpUiVJktVqVZMmTbR3794iNytnspE5USBvPErC+G5NNO7rHXLmuvKdswQZGnN943L1/ZY2fnYoDtYLfMVaQXGwXuCr0t54nG0tWrTwmrG3d+9e2e32Uz7ktGHDBk2ePFmvv/66YmJizONLly5VRESEPvjgA0lSRkaGBg0apIceekgDBw48O99EKfMMD3dFRBZ6jSvU3epXDA8HAABAAAm4xEb16tWVnZ3tdWz//v1q166d6tWrV+hm5XQ3MueaG5rGqn50uFeFhsfcOy7SBbEV/BAVAAAAcGbatm2r1NRULVq0SDfffLNmz56t9u3by2KxKC0tTSEhIfkeejp8+LAGDRqkAQMGqHnz5krPG6YdERGhVatWeV07fPhw9evXTx07diy17+ms88zYCA8v/Jowd5tagxkbAAAACCB+HR5ekE6dOikuLk4ffvihDh48qPfee09bt25Vx44dvTYrkrw2K0WdAwAAAFC+Wa1WTZgwQWPGjFH79u31zTffaMSIEZKknj176vvvv8/3nqVLl+rIkSOaMWOG2rRpY/6RpFq1ann9CQkJUUxMjKKiokr1+zqbjHRPxUbhw8M9FRtGZkapxAQAAAD4wnC5Tm68VPqaNGmiVatWmcO///zzT02dOlVbt25VTEyMRo4cqS5dukiSVq5cqREjRigiIkJOp1Pz5s1To0aNTnnOV4cPp3q1orLZLOWupUNCarb6zftDMqQj6Q5VCrPKYhh69642iq0Q4u/wyqzyuFZw9rBe4CvWCoqD9QJfldRaMQwpJiawKn4TEhK0ceNGtWnTRtHR0f4OJ6D3F6Fz3lCFkY8rq+ctSn3r3QKvMY4cUUzT+pKkQweSJR4cKzWBtFYQ+Fgv8BVrBcXBeoGv/LG/CIhWVNu3b/d6fdFFF+mjjz4q8NouXbpo+fLlBW5WijqH42IrhOiLAe304z9H9OSSrYqJsOndO9vIZg24Ah4AAACgWGJjYxUbG+vvMMoET8WGimhF5QoLO/4iM1OKLHweBwAAAFBaAiKxUVxFbVbYyPjGZg1S8xruMvr4IxnK9X/hDgAAAIBSdHx4eOGtqBQaevz6rCy5SGwAAAAgAPCI/jmsWqRNVSJscrqk7Ylp/g4HAAAAQCky0j3Dw4tIbAQFyRXibldrZGWWRlgAAADAKZHYOIcZhqGmse4nrrYkkNgAAAAAziU+VWzoxAHiJDYAAAAQGEhsnOOaVXcPY9lyMNXPkQAAAAAoTZ4ZG64iZmxIkiuvHRUVGwAAAAgUJDbOcSQ2AAAAgHOTWbFRVCsqSfIMEM/MOssRAQAAAL4hsXGOa5bXimp3cqbSsnP8HA0AAACA0mLO2DhVK6q8xAYVGwAAAAgUJDbOcZXDbaoR5R4GuDWBqg0AAADgXOFrxQatqAAAABBoSGzghHZUDBAHAAAAzhUMDwcAAEBZRWIDahbLnA0AAADgXGO2ojrF8HDlVWyIxAYAAAACBIkNmBUbtKICAAAAzh0+t6IKcyc+jCyGhwMAACAwkNiALsgbIH4gJVvJGXY/RwMAAACgNPjeiooZGwAAAAgsJDagyBCr6lZ2981lzgYAAABwDsjNlZHhaUV1qoqNvBkbVGwAAAAgQJDYgKQTB4jTjgoAAAAo9/KSGlJxZmxkFH0dAAAAUEpIbEDSCYkN5mwAAAAA5Z5ZrWEYUl5FRmFcoXkVG5lUbAAAACAwkNiAJKlp3pyNLQdT5XK5/BwNAAAAgLPJM19DYeFSUNHbQmZsAAAAINCQ2IAkqUm1SFkMKSnDoYTUbH+HAwAAAOAsMtJ9GxwuSa4wd6sqZmwAAAAgUJDYgCQpNNiiBjHuTc3WBAaIAwAAAOWZp2LjlPM1JCksb8YGFRsAAAAIECQ2YGKAOAAAAHBuMCs2wn2o2PDM2KBiAwAAAAGCxAZMzU6YswEAAACg/DKHh/vSisozYyODig0AAAAEBhIbMHkqNrYmpDFAHAAAACjHjreiKk7FBokNAAAABAYSGzCdHxMhm8VQanaO9h6lzBwAAAAor4ozPFzh7sSGaEUFAACAAEFiAyarJUiNq9GOCgAAACjvzFZUPgwPp2IDAAAAgYbEBrw0jc0bIJ5AYgMAAAAor4rXiipvxgYVGwAAAAgQJDbgpVl1KjYAAACA8q44rajMio28Kg8AAADA30hswItngPi2hDQ5cxkgDgAAAJRHxys2Tt2KSmHuig1mbAAAACBQkNiAl7qVwxUebFFWTq7ik3giCwAAACiPjldsRJ7yWleYO/nBjA0AAAAEChIb8GIJMtQklnZUAAAAQHlWvOHheTM2cnKknJyzGhcAAADgCxIbyKeZZ4A4iQ0AAACgfMoo/owNiaoNAAAABAYSG8jHM0B8a0KanyMBAAAAcDYUZ3i48io2JEkZJDYAAADgfyQ2kI9ngPjOQ2lyOHP9HA0AAACAkuZpRSVfhocbxvF2VFRsAAAAIACQ2EA+51UMVcVQqxxOl3YeSvd3OAAAAABKmJHurs52hftQsSHJFeZuR2VkZZ21mAAAAABfkdhAPoZhqGnenI2tCczZAAAAAMobc3i4L62odHzOBhUbAAAACAQkNlAgz5wNBogDAAAA5Y+Z2PC1YsMzZyOTig0AAAD4H4kNFMgzZ2PLQQaIAwAAAOWN2YrKx4oNeSo2MjPOVkgAAACAz0hsoECeVlT/HElXpsPp52gAAAAAlBi7XUZOjiTJ5cvwcEmuMM/wcCo2AAAA4H8kNlCgahVCFBNhU65L2pFI1QYAAABQXniqNaTitKJixgYAAAACB4kNFMrTjmozczYAAACAcsOcr2GzScHBPr3HFeZObIiKDQAAAAQAEhsoFAPEAQAAgPLn+OBw39pQSTphxgYVGwAAAPA/EhsolGfOxtYEWlEBAAAA5cXxweGRPr/HFZo3Y4PEBgAAAAIAiQ0UqlleYmN3cqZSs3L8HA0AAACAknA6FRueVlTM2AAAAEAgILGBQlUKD1bNiu4ns7Ym0I4KAAAAKA+MjHRJvg8Ol45XbDBjAwAAAIGAxAaK5KnaYM4GAAAAUD4Y6XmJjQjfExsKc1d30IoKAAAAgYDEBopkDhBnzgYAAABQPpxOKyrPjA0qNgAAABAASGygSM2q5w0Qp2IDAAAAKBdOb3g4MzYAAAAQOEhsoEgXxEbKkHQwNVtJGXZ/hwMAAADgDJ3e8PC8GRu0ogIAAEAAILGBIkXYrKoX7d7wMGcDAAAAKPs8w8NVjMSGqNgAAABAACGxgVNqmjdnY+tB5mwAAAAgcO3YsUO9e/dW27ZtNW3aNLlcrlO+Z8GCBbriiivUvHlz9e/fX4mJiT6dK8uODw8vRiuqsLzERiYzNgAAAOB/fk9sJCcn65prrtHevXsLPH///ffrs88+M18XtVk5nY0MTq1ZrHvOxpYEKjYAAAAQmOx2uwYNGqTmzZvr008/VVxcnNc+oiDr1q3TzJkzNX36dK1atUrZ2dmaNm3aKc+VdafVioqKDQAAAAQQvyY2kpKSNGjQIO3bt6/A81988YV+/PFH83VRm5XT2cjAN54B4lsOppIsAgAAQEBavXq10tLSNHLkSNWpU0fDhw/XwoULi3xPfHy8xo4dq/bt26t69erq1auXNm3adMpzZZ05PPx0ZmxkUbEBAAAA//NrYmP48OHq1q1bgeeOHj2qadOmqX79+uaxojYrp7ORgW8aVY2QJchQUoZDCanZ/g4HAAAAyGfbtm1q1aqVwvJaJjVp0kRxcXFFvqdPnz7q2rWr+To+Pl5169Y95bmyzqzYKEYrKnPGRmbG2QgJAAAAKBa/JjYmTJigfv36FXhu2rRp6tKliy666CLzWFGbldPZyMA3ocEWNaySN0A8gTkbAAAACDxpaWmqVauW+dowDAUFBenYsWM+vT85OVkLFizQHXfcUaxzZZGRfjqtqNwVGwYVGwAAAAgAVn9+8dq1axd4fO3atVqzZo2WLl2qiRMnmseL2qwUda5ixYo+xxQcbPF6bbVaCrny3NLivCjtOJSu7YfSdH3zWH+HE5BYKygO1gt8xVpBcbBe4KvyuFYsFotsNpvXsZCQEGVlZfm0Hxg3bpxat26tTp06FevcqQTi/iIo0z083FIxSjabb/EERbmrO4ysTJ/fgzMTCGsFZQfrBb5iraA4WC/wlT/Wil8TGwXJzs7WmDFjNHbsWEVGepdGF7VZOdONjIfD4dTJYyTsdmfxvolyqElV97/Fxn0p/DyKwM8GxcF6ga9YKygO1gt8VRJrxTBKIJASUrFiRe3cudPrWHp6uoKDg0/53oULF2rdunVatGhRsc75IhD3FxHp7sSGwxYqh4+xBFny9lqZWX6P/1zCzxrFwXqBr1grKA7WC3xV2vsLv7aiKsirr76qCy+8sMCnoSpWrKikpCSvY57NSlHncOY8A8S3JqQqlwHiAAAACDAtWrTQX3/9Zb7eu3ev7Hb7KR9y2rBhgyZPnqwXXnhBMTExPp8ry06nFZXyWv4aWZnKl6kBAAAASlnAVWwsWbJEycnJuuSSSyRJWVlZ+uqrr7RhwwZdd911XgPBT9ystGjRotBzOHMNq4QrxBqktGyn9h7NUp3KYf4OCQAAADC1bdtWqampWrRokW6++WbNnj1b7du3l8ViUVpamkJCQvI99HT48GENGjRIAwYMUPPmzZWeV8kQERFR5Lmyzshwz80rzvBwc8aG0yk5HNJJ1fIAAABAaQq4xMb8+fOVk5Njvp4+fbpatWqlW265RVFRUYVuVorayODMWS1Balw1QhsPpGrLwVQSGwAAAAgoVqtVEyZM0IgRIzR9+nQ5nU7NmzdPktSzZ0+NGjVKXbp08XrP0qVLdeTIEc2YMUMzZswwj2/fvr3Ic2WdkXE6w8OP3/8bWZlykdgAAACAHxkul//riJs0aaJVq1Z5Df/2eOqpp3TppZeqV69ekqSVK1dqxIgRioiIMDcrjRo1OuU5Xx0+nOpVWW2zWegll+e/3/6tBev3q2+b8zT86ob+DifgsFZQHKwX+Iq1guJgvcBXJbVWDEOKialQAhGVnISEBG3cuFFt2rRRdHS0v8MJvP1Fbq6qVq8kSTq85R+5fG2x5XIppnolGS6XDm/cKVds7NmLEZICYK2gTGG9wFesFRQH6wW+8sf+IiAqNop66mnq1Kler7t06aLly5cXuFkp6hzOXNNY96LacjDVz5EAAAAABYuNjVUsv3QvXF61hlTMGRuG4Z6zkZHhrtg4C6EBAAAAvgqIxEZxFbVZYSNz9pw4QHzQgr809KoG5jEAAAAAgc9sQ+VJVBSDKzRURkaGjKyssxEaAAAA4LMgfweAsqNudJgibBbZnS79vveYlm1J8HdIAAAAAIrBSM8bHB4e4a7CKAbPnA0jM+MUVwIAAABnF4kN+ORASpa2J6Z5DQ1fvv2QtiWkamtCqg6k8NQWAAAAEOg8FRsqThuqPK7QUPdnULEBAAAAPyuTrahQ+nq++Wu+Y8kZDt09b735+rcRV5ZmSAAAAACKyUhPlyS5IiKK/+a8ig1lZpZgRAAAAEDxUbEBn4zv1kSWoIJL1S1BhsZ3a1LKEQEAAAAoLiMjL7ERXvzEhitvJgcVGwAAAPA3EhvwyQ1NYzX3josKPDf3jot0Q1MGtgMAAACBzhwefjqtqMzEBhUbAAAA8K/TbkX1999/a9++fapQoYLq1aun6OjokowLAAAAAFDCzOHhp9GKyjNjQ1RsAAAAwM+KndhYvHixZs+erRo1aqhWrVrKzMzU5s2bVbNmTT355JNq2LDh2YgTAaByuE1VwoMVExmiXUkZysrJVWSIRZXDbf4ODQAAAIAPjldsnP6MDXMAOQAAAOAnPic2XC6XRo4cqfT0dM2dO1dVq1b1Ov+///1PAwcO1PDhw9W9e/cSDxT+F1shRF8MaKdgi6HXf96lt9fuVu1KYaoWSWIDAAAAKAvOqBVVXsUGMzYAAADgbz7P2HjyySdVsWJFvfzyy/mSGpLUqVMnzZ8/Xy+99JLWrl1bokEicNisQTIMQ7e3rimbxdDWhDSt33fM32EBAAAA8MHxVlSRxX6vK8ydDGHGBgAAAPzN54qNG2+8UR07dizymtjYWL3xxhuKioo648AQ2KLDberRvLo+23BA7/+2V21qVfJ3SAAAAABO4cyGh1OxAQAAgMDgc8XGqZIaHgwSP3fceUktGZJ+/CdJ/xxJ93c4AAAAAE7hTIaHe2ZsiIoNAAAA+JnPiQ1f5Obm6o8//ijJj0QAq1M5TFc3ipEkzfttr5+jAQAAAHAqZzI83JyxkUFiAwAAAP5VrMRGmzZtlJaWZr4eM2aMjh49ar5OT0/XnXfeWWLBIfDd3baWJOmrrYlKTM32czQAAAAAinJmw8PdFRvM2AAAAIC/FSuxkZF3E+zx1Vdf5TvmcrnOPCqUGRfWiFLrWhWVk+vSR3/s83c4AAAAAIpwJq2oXGGexAYzNgAAAOBfxUpsGIbh9bqgJMbJ16D8u/sSd9XGZxsOKC07x8/RAAAAACiUp2LjdGZshDFjAwAAAIHhjGZsGIZBIgPq0CBa9auEK93u1OcbDvg7HAAAAACFMNLTJZ1mxYZnxgYVGwAAAPAza3Eudrlcuvrqq81kRkpKim688UYFBbnzI7m5uSUfIQJekGHo7ktqafw3O/ThH/v0nzbnKdhSonPpAQAAAJQAI8Od2NBpDQ/Pa0V1UjtiAAAAoLQVK7Hx3nvvna04UMZd37SaXvvpXx1Ks+vrrYm68cLq/g4JAAAAwEnObHg4FRsAAAAIDMVKbFx66aWSJLvdLpvNZh5ft26dUlNT1apVK0VHR5dshCgTgi1B6tvmPL20Ol7vr9ur7s1jFUSbMgAAACCgnEkrKnmSIczYAAAAgJ8Vq1/Q33//rV69eumhhx6S5G49NXToUPXv31+TJ0/Wddddp99+++2sBIrAd0vLGoqwWRR/JEM/xyf5OxwAAAAAJ3K5zFZUrtNqRUXFBgAAAAJDsRIbo0eP1kUXXaRp06ZJkt5//3398ssvWrRokVasWKERI0ZoypQpZyVQBL7IEKt6tawhSXrvt71+jgYAAACAF7tdRk6OpNMdHp43Y4OKDQAAAPhZsRIbO3bs0IMPPqiYmBhlZWXpjTfe0JAhQ9SgQQNJ0jXXXKP4+PizEijKhv+0OU/WIEPr9x7TpgMp/g4HAAAAQB5zcLjOsGIjk8QGAAAA/KtYiY0LL7xQn3/+uQ4dOqTJkyfLYrHo9ttvN88vW7ZMtWvXLvEgUXZUqxCi65tWkyS9T9UGAAAAEDDMweE2m2Qt1rhFtzB3xYYyMyWXqwQjAwAAAIqnWImN8ePHa9myZerYsaNWrVqlF198UTabTXFxcercubNefvlljRw58mzFijLirktqSZK+23lYu5N5mgsAAAAIBGc0OFySKy+xYbhckt1eYnEBAAAAxVWsx3Tq16+vL774QkePHlVUVJSCgtx5kSpVqmjkyJFq3bq1qlSpclYCRdnRMCZCVzSI1o//JGn+73v1VJdG/g4JAAAAOOedyeBw6fiMDck9Z8MVElIicQEAAADFVayKDUlyOBxKT083kxo5OTn6/vvvdfDgQR09erSk40MZdXdbd9XGkk0HdSSdp7kAAAAAfzNbUYWHn94HBAfLlbcPNLKySiosAAAAoNiKldhYt26drr76aj377LOSpOzsbN11112aNm2aVq5cqVtvvVXffPPNWQkUZUvr8yrqwhoVZHe69PGf+/0dDgAAAHDOM9LTJJ1+KyoZhhR6wpwNAAAAwE+KldgYN26c7r77br311luSpDfeeEMHDx7U0qVLNXfuXE2fPl0zZ848K4GibDEMQ3fnzdpY+Od+Zdidfo4IAAAAOLcdr9g4zcSGJFdYqPuzSGwAAADAj4qV2Ni3b59uueUWGYahY8eO6d1339Wjjz6q6OhoSVLLli114MCBsxIoyp6rzo9R7UqhSsnK0RebDvo7HAAAAOCcdqbDw6XjczaMLBIbAAAA8J9iJTbatWunWbNm6c8//9RTTz2lqlWrqmfPnpIkp9Opd999V02aNDkrgaLssQQZuiuvamP+73uVk+vyc0QAAAAIRDk5Ofr000+LvGbNmjVyOqkCPiNnODxcklxhnsQGMzYAAADgP8VKbEyYMEEJCQnq37+/EhIS9MorrygoKEjbtm1Tq1at9NVXX2nMmDFnK1aUQd2axapyWLAOpGTrvV9266GP/9KWg6n+DgsAAAABxDAMTZ8+XW+++aa++OILbdy4UTk5Oeb5ffv26dFHH9XatWv9GGXZd8bDw3W8YoMZGwAAAPAna3EujomJ0euvv57veO3atfXJJ5+oYcOGstlsJRYcyr7QYItub1NTr/+0Sx+u36ejmTlatiVBzapX8HdoAAAACBAWi0WSlJKSori4OH388cf6+++/1bZtW91yyy2aNm2a+vTpow4dOvg50rLtjIeHS1Jo3owNKjYAAADgR8VKbBQmIiJCTZs2LYmPQjlzICVLLWtEyWYxdDTT/dTd8u2H1KN5rFySKoUFq0ZUqH+DBAAAgF9s3bpVdevWVXh4uCpVqqQRI0aY51JTUzVkyBANHjxYV155pR577DE/Rlo+eCo2VBKtqDIzSiIkAAAA4LT4nNh48cUXNWDAAEVGRhZ53bfffquYmBi1bNnyjIND2dfzzV/zHUvOcOjueevN17+NuLI0QwIAAECAGDdunDZv3qyWLVsqLS1Na9eu1a5du/T7779r7dq1ateund577z1NmjRJX331lW644QZ/h1ymlczwcCo2AAAA4H8+z9ioXr26Hn74YaWlpRV6zc8//6zJkycrOjq6RIJD2Te+WxNZgowCz1mCDI3vxrB5AACAc9VHH32kH374QXfffbeuuOIKDRkyROPHj1fVqlW1bNkyPffcc7r00kv10ksvadKkSUpKSvJ3yGWaYQ4PP4MZG2F5781ixgYAAAD8x+fERt++fXXTTTepb9++WrlypZxOp3kuMTFR06ZN06RJkzRr1izVqlXrrASLsueGprGae8dFBZ6be8dFuqFpbOkGBAAAgICxcOFCfffdd8rJyZHFYtH333+vbt26KTU1VZ988okSEhIkSXPmzFHPnj31v//9z78Bl3HHh4eXwIyNTCo2AAAA4D/FmrHRq1cvXXzxxZozZ46mT58um80mu92u8PBw9ejRQwsXLlRYXs9V4GSGJNcJrx1OV2GXAgAA4BzQtGlTzZ8/X06nUzabTU8//bRiY2P11FNPqXXr1nr33Xd1wQUXSJKefvpp2Ww2P0dctpVMK6q8GRtUbAAAAMCPij08vG7duho/frwkyW63s7nAKVUOt6lKeLBiK4To2ibV9MoP/8jpkr7ckqAWNaP8HR4AAAD85Ndff1WTJk0UHx+vv//+W/Xq1VPt2rXlcrlUtWpVLVmyRAMHDlQW8xxKxPFWVGcyPNxTsUFiAwAAAP5T7MTGiUhqwBexFUL0xYB2CrYYMgxD51UM0RNLturzDQfUrVmsWpLcAAAAOCft3LlTNptNBw8e1G+//abg4GCFhISob9++ysrKksPhUGZmplq3bq1XX31Vjz76qL9DLtPMVlRnULGhvIoNZmwAAADAn3yesQGcCZs1SIbhHiJ+deOq6tasmnJd0rivtyvL4TzFuwEAAFAejR07VqGhoerQoYNatWqlgwcP6s4779Rjjz2mlJQU3XTTTQoODtZjjz2mr7/+WkePHvV3yGWa2YrqjIaHe1pRUUUDAAAA/yGxAb8YcXVDVYu0aXdypl75Id7f4QAAAMAP3nrrLTkcDlWsWFENGjTQhAkTNHToUDVp0kTR0dH69NNPNXToUAUHB6tr16768ssv/R1ymVYirag8MzZoRQUAAAA/OqNWVMDpigoN1tPXNdbQTzdpwfr96nR+jC6pU8nfYQEAAKAUDRo0SEFBQUpISFD9+vXVokUL3X///bJYLLrrrrtUqVIltWvXTpLUp08fVatWzc8Rl20lMzw8b8YGFRsAAADwIyo24DeX14vWLS2rS5LGf7Nd6fYcP0cEAACA0hQU5N6OxMbGqkWLFpKkm2++WeHh4erfv7/XtbVr11ZISEipx1hu5OaaVRZnNjw8b8ZGZkZJRAUAAACcFhIb8Kv/u6qBakaF6EBKtmb87x9/hwMAAIBS5nTmn7f2008/admyZZKkgwcPas6cOaUdVvmTcTwRcSYzNkTFBgAAAAIAiQ34VYTNqmevbyJJWrTxoH6OT/JzRAAAACgtY8eO1datW72OHTlyRKNGjZJhGHI4HHr44Yf1888/Kzc3109Rlg9mGyrDkDxVF6fBFeZOihhZzNgAAACA/5DYgN9dXLuS/tPmPEnSxOU7lJLl8HNEAAAAKA0bNmxQenq6kpKStGbNGknSxIkT5XQ6dcMNN+i///2vXC6XXnnlFbNtVVF27Nih3r17q23btpo2bZpcLtcp37NgwQJdccUVat68ufr376/ExETz3K+//qobbrhB7dq10zvvvHP632gA8Bocbhin/TnmjI1MKjYAAADgP8VObGzZskVxcXGKj48v8M/OnTtlt9t9/rzk5GRdc8012rt3r3ls5cqV6ty5s5o1a6Y+ffooLi7OPFfUZuV0NjIIDA9fUU91KofpUJpd//027tRvAAAAQJnXokULHTlyRD/88IPefvttORwO1a9fXxaLRZKUmJiomTNnKsyHCgO73a5BgwapefPm+vTTTxUXF6fPPvusyPesW7dOM2fO1PTp07Vq1SplZ2dr2rRpkqSkpCQ99NBD6t69uxYsWKAlS5Zo7dq1Z/5N+0lJDA6XJFdo3r8FFRsAAADwo2InNm699VYNHDhQ999/f74/N954owYNGqSEhASfPispKUmDBg3Svn37zGO7d+/WqFGjNGLECK1evVo1a9bU6NGjJRW9WTmdjQwCR2iwRWOvb6IgQ/pqa6K+23nY3yEBAADgLGvYsKESEhLUs2dPpaenKy4uTkOHDlVQUJBcLpdefPFF1a5d26fPWr16tdLS0jRy5EjVqVNHw4cP18KFC4t8T3x8vMaOHav27durevXq6tWrlzZt2iRJ+uKLL1S1alU9/PDDqlevngYPHnzKzwtkhmfGxpnM15CkMGZsAAAAwP+sxX1DVFSUVq1aVeC5rl27avny5T5/1vDhw9WtWzf9+eef5rG4uDgNGzZM3bp1kyT17dtXDzzwgCTvzUpYWJiGDx+ucePGqXfv3kWeQ9nQomaU7m5bW+/+ukdTVuzURedFqXK4zd9hAQAA4CypVauWfv31V2VmZmrChAmaNm2aZs+erbCwMD3xxBOyWt3blbCwMN1222264IILCv2sbdu2qVWrVmZ1R5MmTbwqvwvSp08fr9fx8fGqW7euJGn79u267LLLZOS1bWrZsqVeeOGF0/5e/c2rFdUZ8FRsGJkZp7gSAAAAOHuKXbFhFNGPtahzBZkwYYL69evndezqq69W3759zdcnbi6K2qyczkYGgWfg5XXVMCZcyZkOTV35N+3EAAAAyrHq1atr0aJFeuaZZ9SwYUOtXr1av//+u8LDw9W8eXO1atVKrVq1UmJiol555ZUiPystLU21atUyXxuGoaCgIB07dsynWJKTk7VgwQLdcccdBX5eZGSkz5XpgajkWlFRsQEAAAD/K3bFRmZmZoFPKrlcLiUnJ+uFF15Qnz59fCoZP9U1drtdb7/9tu69915JRW9WijpXsWJFH787KTjY4vXaarUUciXOBpvNokk3NtNd7/6ub3ce1rd/H1Gd6DC9+F2chl3dUM1rRPk7xEKxVlAcrBf4irWC4mC9wFeBslYqVKigyMhIxcfH66uvvlKdOnX04YcfKjIyUj179lR0dLQkqVKlSpozZ06Rn2WxWGSzeVf7hoSEKCsry6f9wLhx49S6dWt16tSpwM/zfFZxBcr+ItieNxMjMkI22+nHYFSMdP9vZqZswUFnNIgcRQuU/5+ibGC9wFesFRQH6wW+8sdaKXZiY+DAgQoNDZXVavV6mt7lcunhhx+W3W5XUFCxC0EKNGPGDIWHh+u2226TVPRm5Uw3Mh4Oh1MnFwnY7c7T+wZwWhpGh6v/ZXU0++ddmrx8hzqdX0W/7TqqxX8dUKMqZ/aE2dnGWkFxsF7gK9YKioP1Al+VxFo5099pR0VFKSwsTO+8847uvfdeGYahBg0aaO3atcrOztaOHTvUuHFjXXHFFbruuuuK/KyKFStq586dXsfS09MVHBx8yjgWLlyodevWadGiRV6fl5SUVOzPOlmg7C+CUtIkSblhEWf09Q3L8T2XPTVDyqvgwNnBf9NRHKwX+Iq1guJgvcBXpb2/8DkD8ffff+uPP/7Q4MGD1b9/f82ZM0f9+vVT7969tW3bNm3ZskX9+vXTgAEDdN55551O3F5++uknffTRR3r++efNDcTJmwvp+AajqHMoe66/oKrqRYcpJStHX21JlCQt335I2xJStTUhVQdSKH0HAAAo60JDQ5WZmamKFSvqvvvu09GjR9WtWzcdOXJEqampeuyxx/Tmm28qMjLylG1vW7Roob/++st8vXfvXtnt9lM+5LRhwwZNnjxZL7zwgmJiYgr9vK1btyo2NvY0v1P/M1tRneHwcM+MDUkysjLP6LMAAACA0+VzYuPTTz/VN998Y742DEMOh0P/+c9/lJKSouHDh5dYUHv27NFjjz2msWPH6vzzzzePF7VZOd2NDAJTr7fX6d8k90bJket+xC05w6G7563XPfPWq+ebv/ozPAAAAJQAq9WqtDR3JUHHjh01ZswY1atXT5GRkUpOTtaLL76o+fPne1VSFKZt27ZKTU01r509e7bat28vi8WitLQ0ORyOfO85fPiwBg0apAEDBqh58+ZKT09Xel4C4JprrtHvv/+utWvXKicnR2+//bauuOKKEvveS1tJDQ9XcLBcFnerASOTxAYAAAD8w+fExmWXXaZ169Zp4cKF+uSTT5Sdna3FixerdevWuvrqq/Xjjz+a5z744IPTDigrK0sPPvigunTpos6dO5ubC5fLVeRmpahzKHvGd2siS1DBT+VZggyN79aklCMCAABASbNYLHr00UclSZUrV1a3bt0kSYsWLVK7du3UsGFDvfHGG5oyZYqOHDlS5GdZrVZNmDBBY8aMUfv27fXNN99oxIgRkqSePXvq+++/z/eepUuX6siRI5oxY4batGlj/pGk6OhoPfnkk3rggQd0xRVXaOfOnXrooYdK8LsvXSU1PFw6oWqDxAYAAAD8xHC5Tu74WrCEhAR17dpVt9xyi1wulxYtWiTDMJSdna1LL71U9evXl8vlksvlksPh0JQpU3wOokmTJlq1apVq1aqllStX6uGHH853zYnnR4wYoYiICDmdTs2bN0+NGjWSpCLP+erw4VSvHrg2m4Vecn6yLSFVd89bn+/4+3e11gWxFfwQUdFYKygO1gt8xVpBcbBe4KuSWiuGIcXEnP37sv3796tmzZo+XZuQkKCNGzeqTZs25vDxM7Fr1y7FxcXp0ksvVWRkZLHfHyj7i8gnhyvsnbeUPuJJZTw5+ow+q0qzhgo6fEhJ/1sjZ7PmJRQhTsZ/01EcrBf4irWC4mC9wFf+2F/4PDw8NjZWQUFBeuaZZ2SxWPTjjz/qiy++0Lvvvqv3339fbdu21eDBg09rcPj27dvNv3fp0sXr9cm6dOmi5cuXF7hZKeocyi5D0onZt5SsHH+FAgAAAD/wNakhufctJTkLo27duqpbt26JfZ6/GBkZkiRXRPGTMydzhbkrNpixAQAAAH8pVhaiRo0aOnr0qCTJ4XAoIiJCgwcP1sKFC/Xrr7+qX79+OnTo0NmI00tsbKy6dOlSYOKiqHMoWyqH21QlPFhNYyM19Mr6Cra4W1O98kO8snNy/RwdAAAAzpSnynrXrl16/PHHtXnzZvPcJ598omuuuUb333+/HyMsP0pqeLgkuUJD3Z+ZlXXGnwUAAACcjmIlNhYtWqQqVapIkm6//Xbz+Hnnnad33nlH559/vpn4AM5UbIUQfTGgnebe2Vp3t62teXe1UYQtSFsT0jThm+3ysYsaAAAAApRhGFqwYIFyc3N18OBBTZ06VbfddpuGDh2qlStX6o033tDevXv1448/+jvUMu/48PCSSGzkVWxkZpzxZwEAAACnw+dWVJJks9nMv588B8NisWjMmDElExWQx2Y9nntrEBOh6T2ba+hnm/TNtkOqUzlMA9vX819wAAAAOCNBQUGyWCySpNatW2v48OHavXu3Nm7cqO7du0uShg0bpkaNGik7O1shISH+DLdMOz48/MxbUSmvYkOZVGwAAADAP4qV2HC5XBo1apTGjRvnleQASsuldStrZJfzNXH5Tr25ZrdqVQpTt2Yl10MZAAAApcswDBmGoXnz5unjjz+WYRhyuVyaOHGiJPcexOVyyeFw6I8//vBztGWYOWOjBCo2wtyfwYwNAAAA+EuxEhuGYWjx4sWaNGmSeSwrK0uhnid2gFJwU4sa2p2cpfd+26OJy3eoRlSoWteq6O+wAAAAcJpq1Kihjz/+WFWqVJFhGAoNDVVOTo6CgoKUkpKi6tWr+zvEMu94K6qSGB7OjA0AAAD4V7FmbEju5EZQ0PG3tW7dWpdeeqm6d++u+++/X6NGjdJnn31WokECJ3u4Yz1d0yhGDqdLjy/erD3JPC0GAABQluzatUsDBw6UJB05ckRz587VgAEDdMMNN+jrr7/WNddco59++kmTJ0/2c6Tlg6cVlUqiYiNvxoao2AAAAICfnFZi40RWq1XvvvuunnzySXXv3l3VqlXTpEmTtHLlyhILEjhZkGFo3A1N1Kx6BR3LytGjn2/SsUyHv8MCAACAj8LCwtSxY0dJ0jfffKOQkBC5XC498cQTcrlcqly5subNm6dt27Zp/vz52rhxo58jLtsMTyuqEhge7pmxYWSQ2AAAAIB/FDuxke8DgoLUtGlTXXnllerVq5ceffRR9e7dWz/99FNJxAcUKjTYoudvbq7qFUK0OzlTTy7ZIocz199hAQAAwAfVqlXT3XffLZfLpV9++UXS8XkbkhQTE6NOnTrp2LFj2rJliwYNGuTPcMs2l0tGepr7ryUwPNxTscGMDQAAAPiLz4mNtLQ0JSUl+XRt1apVlZOTc9pBAb6KibDpxVsuVITNot/3HNOkFTvlcrn8HRYAAAB8ZBiGxo8fbw4J//bbbyVJmZmZuu+++1SvXj098cQT6tatmzIz+UX6abHbZTidkkqmYsMV5klsMGMDAAAA/uFTYmPp0qW6/vrr9fbbb/v0of369dOECRPOKDDAV+dXjdDkHk1lMaQvNydo7q97JElbDqbqoY//0paDqX6OEAAAAEVxOByy2+2688471apVKzVu3FgXXHCBJKl69eravHmzRo8erbC8X6ijeDyDwyXJFR5xxp/nGR7OjA0AAAD4i0+JjczMTA0dOlTDhw+Xy+VScnKyUlNTC31iymazlWiQwKm0rx+tx645X5L06o//asX2Q1q2JUHr9hzTsi0Jfo4OAAAAhdm/f7/eeOMNBQUFacOGDdq3b58WL16sqlWr6qOPPtLVV1+tJk2a+DvMMs0zONwVEiJZrWf+gaFUbAAAAMC/fLqr7dOnj9fr9u3bm393uVzq3LmzLr74Yt18881e54DSdOtFNbX1YKq+2JygZ5dtVViwe3kv335IPZrHyiWpUliwakSF+jdQAAAAmP7zn//IZrMpODhYVqtVhmHIbrcrNTVVv//+u3bs2KGnn35at912m5599ll/h1smlejgcJ0wY4PWYAAAAPCTYj+uYxiGtmzZIofDobS0NB0+fFg7d+7UihUrNHDgQLVv317PPfecKlaseDbiBYr0xWZ3dUZOrpSa7Z7zkpzh0N3z1pvX/DbiSr/EBgAAgPyGDRtm/n3z5s1q3rx5vmt27tzJfI0z4GlFVRKDwyXJFep+UIjEBgAAAPzF5+HhJzIMQzabTdHR0WrcuLG6d++uGTNmaPHixUpOTtZdd92llJSUko4VOKXx3ZrIYhgFnrMEGRrfjTYGAAAAgejXX3/V7bffrv3798tut5vHc3NzNXnyZB08eNCP0ZVtZiuqkqrYMIeHk9gAAACAfxQ7sZGTkyOn01nguYYNG2r+/PmqVauWRo8efcbBAcV1Q9NYzb3zogLPzb3jIt3QNLZ0AwIAAMApZWVlafz48Ro8eLAMw1DPnj117NgxSVJGRoauuuoqPf30036OsuwyKzZKKLEhzxB3ZmwAAADAT4qV2MjNzdXw4cOVm5tb6DXBwcF68cUXNXjw4DMODgAAAED5lpycrAceeECXXXaZBg8erBo1aqhJkyZ6++23JUmRkZG69957aXV7BsyKjRJrRUXFBgAAAPyrWImNoKAgDRw4UMHBwUVeFxoaqqZNm55RYMDpqhxuU5XwYDWNjVT9Ku6n0qxBhqJCi163AAAAKF3PPvusbr31VvXq1curImPs2LFasmSJ0vN+IS+59yI4PSU/PNwzY4OKDQAAAPjHGe8OsrKy1LNnz5KIBSgRsRVC9MWAdnr3ztZ6uXcLRdiClJPr0orth/wdGgAAAPKkpKTo6NGjSkxM1E8//aTU1FSlpKTo77//1tGjR9WxY0e9++67io+P16ZNm5ScnOzvkMsuc3h4RIl8nFmxkZlRIp8HAAAAFJfVl4vsdrumTZumZ555RqtXr1aHDh1ksVjcH2C1av/+/Wc1SKC4bFZ3zi62QohGXH2+xn+zQ2/8/K+uaBCthjEls6EDAADA6YuKitJLL72khIQETZkyRT169NDNN9+slStXymq1yjAMSdLy5ctlGIZ69Ojh54jLruPDw0voPjjMXbHBjA0AAAD4i0+JDYvFoqVLl+ree+/VsGHDFBMTo5EjR6pTp06yWq2nbE0F+FOP5rH6dudh/fhPksZ9vV1v971IVgutDAAAAAJBdHS0LrroIjVu3FhvvfWWPvzwQ1mtVjVs2FB//PGH6tSpo5iYGH+HWaaV9PBwV5j7c5ixAQAAAH/x6be7FotFhmGodu3a+u6779SzZ08NGzZMgwYN0rFjx+Ryuc52nMBpMwxDo65tpKhQq7YmpOnd3/b4OyQAAADkCQoK0ty5czV48GA988wzatCggQYPHixJ2rBhg/744w8/R1j2eSo2VGLDw/NmbFCxAQAAAD/xqWJDklkKHhUVpYcffli9e/fW559/rqioqLMWHFBSqkaG6LFrGurZZdv11prd6tigihpXK5mNHQAAAE5PcnKyKleurJCQEEnSp59+qiVLligxMVH9+/dXYmKicnNzNX/+fDkcDn3wwQd+jrhsKvnh4XkzNrKypNxcicHuAAAAKGU+Jzbsdru+/fZbr+qMxo0b69tvv1VOTo55Ljc3V9nZ2fTARcC5/oJq+nbHYf3v7yMa9/V2zb2ztYJpSQUAAOA3N998s2rWrKn09HTt27dPAwcOlM1m06hRozRo0CDt2LFDa9euVb9+/ZSdne3vcMsso4SHh5szNiT3nI0SSpgAAAAAvvI5sZGVlaWXXnqpwHOZmZnmOafTKbvdTmIDAccwDD3VpZHW7z2mHYfS9c4vuzWwfT1/hwUAAHDOWrFihdavX69BgwbpnnvuUaNGjfTggw9Kki699FJFR0dr9erVatu2rZ8jLdtKeni4p2JDcs/ZKKlKEAAAAMBXPic2KlSooEWLFhV47rLLLiv0HBBIqkTY9ETn8zX6y216+5c9urJhFV0QW8HfYQEAAJyTbDabWrdurcqVK2vlypX66quv9NhjjykxMVFOp1PR0dH6+++//R1mmVfSrahktcoVHCzD4ZCRlSUmLgIAAKC0+dyHxzNjQ5ISEhL06KOPauDAgcrJyTkrgQFny7VNqqpz4xg5c10a+/V22XNy/R0SAADAOSsoKEiPPfaYDMNQt27d9Pnnn2vZsmWyWCyqVKmSJk+e7O8Qy7zjrahKbsbc8TkbmSX2mQAAAICvij1g4JNPPlH37t116NAh3XfffbJafS76AAKCYRh6svP5qhwWrLjDGXpr7S5/hwQAAHDOslqt6tatm/k6KipKderUkeROelx22WX+Cq3cON6KqgRbRoXmzdnIzCq5zwQAAAB85FNiw+FwyOFwyG636/vvv9eoUaP0wQcf6PLLL5fkXc0BlAWVw216qsv5kqR3f92jzQdT/RwRAAAAcHaUeCsqSa6wvIqNzIwS+0wAAADAVz4lNoKCgjR16lTZbDa98sor6tWrl3nObrfLbreftQCBs+WaxlV13QVVleuSxn21Xdm0pAIAAEA5ZKSnSSrpVlTuig0ji4oNAAAAlD6fEhsWi0VdunQp+AOCgvTUU0+VaFBAaXnsmvMVHR6s+KQMvfHTv/4OBwAAAChxZ6diw/1ZzNgAAACAPxR7xsbJrFar+vTpUxKxAKWuUliwRl3bWJI0b91eLd54QA99/Je20JoKAAAA5YHTKSPTnXwoyYoNZmwAAADAn4qV2HA6nZo1a5b52uFwqGPHjl7X5Obm6sEHHyyZ6IBScNX5VdS9WTW5JM34/h+t23NMy7Yk+DssAAAA4IydOAOjRCs2zFZUVGwAAACg9BUrsREUFKSFCxfKbrerT58+Cg4OVnBwsCTprbfeMq9Zt25dyUcKnCUHUrLUo3msKoVZlZbtlCQt335I2xJStTUhVQdSTv0U2paDqXpg/noqPQAAABBY0vPaUBnG8SqLEmAOD2fGBgAA+H/27js8qgLr4/j3Tk0nBCQQQIooIE1EBFGxYUEQhdV1X1nU1VWxra64upbVta+9u64VBbtUAQVRRCyoICJIlV6SUNLb1Pv+MUkgpE2SSWYm+X2exwczt52Em2HuPfecIxIGtmBXNE0TwzCwWCxYrVb2798PgGEYAEyePJm//vWvAOXJDpFoMObVHyu9ll3kYcLUFeVfPzbmaNolOGib4KRNvAObxaiw/rw1mfy0LYd5KZkc3T6x0WMWEREREQlGhcHhhlHL2sErr9g4qCJERERERKSpBJ3YuPTSSznvvPOAwDBx45APxXEHlTUfukwkkt1/bk/u+2wDPr9Z7Tq3zV5T/v8WA1LiHCTH2kiMsdM61s73W7MAWLBuL6P7pGISmN/RISl0T8WJiIiIiNRVYwwOhwPDw1HFhoiIiIiEQVCJDb/fz/jx4/nggw+qXcfj8TBt2jT8fj9+vz9kAYo0tpG9U+mWElehQqPMmUe1xeM32VvgZm+Bi/2Fbnwm7Ct0s6/QXWn97OKKlR4/TRreqLGLiIiIiNSkPLERHx/aHZdXbGjGhoiIiIg0vaASGxaLhXPOOYdzzjmHM844o8p1PB4PixYtwjRNJTYkahmAedCflx7fmV6pB1pL+fwm2cUe9ha4+GztHt7/eRdVFXpYLQb3nnNUE0UtIiIiIlK1slZUxIU2sWHGaMaGiIiIiIRP0K2oyuzatYvRo0dTUFDAjz8emE3gdDp54YUXADjhhBNCF6FIE2gd56BNnJ3URCfn92vPrFUZZOa7aB3nqLCe1WLQNt5B23gHvVMTGdm7XZWVHtcM68LI3qlNFb6IiIiISJUarxVVacVGiSo2RERERKTpBZ3YWLt2LYsXL6ZDhw7cc889TJw4kX/961/k5uayfv16CgoKGjNOkUaVmuhk9lVDsFsNDMNgbP8OeHwmDpslqO3LKjzKvPLdVgZ1TqZ/WlKjxCsiIiIiEgyjqBBojFZUgYoN1IpKRERERMIguLu2wL333ovP58NisXD88cfTunVr5s+fj9/v59prr6Vnz57l66oVlUQjh81SPvjeMIygkhpllR69UxO4+5yj6J2agN1q4PXDrTN/Y1euLvREREREJHyMwtLERqhbUcWWtaLS510RERERaXpBJzY++OADrr/++vKvTTPwfHpycjLvv/8+eXl5vPbaa5imSWHph2eR5q6s0mPy+IFcNLAjb40fyLxrhtKzXQLZxR7+Pv038ku84Q5TRERERFqoxhoeXj5jo1gzNkRERESk6QWd2DAMA7/fj9frxePxEFv6hA5Au3btePPNN/noo4/46KOP+P777xslWJFIdGilR3Ksnacu6MNhCQ62ZBVxx5w1eH2qYhIRERGRplc2PDzkFRsxmrEhIiIiIuETdGIDwOfzcdJJJ2G325k7dy4+nw+vN/A0euvWrXn22Wd5+OGH2bJlS6MEKxIt2iU6efqCvsTYLPywLYfHvvy9vMpJRERERKSpNN7w8NIH3UpUsSEiIiIiTa9OiQ273c5DDz1U/rXVauWpp55i1apVAPTq1Ysnn3ySHj16hDZKkSjUMzWBB0f1xgBm/JrBO8t3hTskEREREWlhGm94eGnFRnFRaPcrIiIiIhKEOiU2DvXCCy9w3HHH8Ze//KX8NYfDUeFrkZbslB5tuPnU7gA8t3gzX23cF+aIRERERKQlabTh4WUzNlSxISIiIiJhEHRi44orrmDixIlMnDiRf/3rXwDMnTsXgPiDnv756KOPOOaYY0IbpUgU+79jO/KHAR0wgX/NW8fazPxwhyQiIiIiLURjt6IyijVjQ0RERESaXtCJjc2bN3PdddcxceJEfvnlFyBQnQHgdDoBmDlzJitWrOD6668PfaQiUcowDG49vQdDu7SmxOvnlhm/kZnvCndYIiIiIs3Ohg0b+MMf/sDgwYN59NFHg55xtm3bNo4//vhKr7/22msMGzaMY489lhtvvJHs7OxQh9zoyoeHh7gVVVnFhmZsiIiIiEg4BJ3YcDgc9O/fv0I1hsUS2Nzn8/Hggw/y7LPP8sorr5CUlBR0ANnZ2Zx++uns3Lmz/LWaLkjqu0wknGwWg0fO6033NnHsK3Tz9xmrKXL7wh2WiIiISLPhdruZOHEiffr0Ydq0aWzatInp06fXut2OHTu4+uqryc3NrfD6Tz/9xMyZM5k6dSozZszA5XLx6KOPNlb4jeZAxUaIZ2zEqhWViIiIiIRP0IkNwzAqvbZ582YGDRpEeno6S5Ys4eOPP6Z3795BHzwrK4uJEyeya9eBoco1XZDUd5lIJEhw2nh6bF9S4uxs3FvIXXPXsnp3Htd+uJI1GWpPJSIiItIQX3/9NQUFBdxxxx0cfvjh3HLLLXz88ce1bnfNNddw0UUXVXr9119/Zfjw4XTv3p0uXbowatQotm7d2giRN7KyxEbIKzY0PFxEREREwqdBw8MPP/xwZs6cSUpKCu3atePSSy9lx44dQW9/yy23cO6551Z4raYLkvouE4kUaa1iePKCPjhtFr7ZnMUjCzeybEcu89Zkhjs0ERERkai2bt06BgwYQGxpJUHPnj3ZtGlTrdv973//45xzzqn0+pFHHsnnn3/O9u3b2b9/Px9//DHDhg0LedyNrbwVVWMND3e7wadKZBERERFpWkEnNoqKili8eDFfffVVhdc7d+5MfHw8U6ZMYeTIkUyYMIG8vLyg9vnAAw9w2WWXVXitpguS+i4TiSRt4h1cNfRwADbsLQRgwfq9rMvMZ21mPul5KucXERERqauCggI6depU/rVhGFgslkotpg7VuXPnKl8fPnw4Xbp04cwzz2TYsGEUFxdz9dVXhzTmplDWior4xhkeDmjOhoiIiIg0OVuwKw4aNIh58+ZhGAYnn3wyEEh2ABQXFwNwww03sG3bNp544gnuv//+WvdZ1UVETRck9V3WqlWrYL9N7HZrha9tNms1a4pUFOy5MubVHyu9ll3kYcLUFeVfr7zjtJDFJZFJ7y0SLJ0rUhc6XyRYzfFcsVqtOByOCq85nU5KSkrqdD1QZt68eezevZtPP/2UlJQUHn30Uf7xj3/w/PPP12k/4b6+sBQFHqSxJSdhcYTw2LaE8v91+t2YjuDnLEpwmuPvqTQenS8SLJ0rUhc6XyRY4ThXgk5sPPPMM5imWWHWxmuvvQbAqaeeyuLFi8nMzGTixIlcc801+Hw+rNa6f0M1XZDUd1ldLmQ8Hh+Hzhx3a8izBCmYc+X+c3ty32cb8PkrD7e3Ggb3jjxK51wLob9nCZbOFakLnS8SrFCcK1WM4QubVq1asXHjxgqvFRYWYrfb67W/uXPn8n//9390794dgLvuuotBgwaRl5dHUlLwN/HDen1hmlAYSGy47TH4Q3xc0+HAcLvx5BXiT2wd0n1LgN7TpS50vkiwdK5IXeh8kWA19fVF0K2oli1bxplnnklGRgYFBQUsXryYXbt28c0333DWWWexZcsWHnroIVauXMnMmTPrldSAwAVJVlZWhdfKLkjqu0wkkozsncrkS46pclmbeDvdUkLbJkBERESkJejXrx8rV64s/3rnzp243e56VWsA+Hw+9u3bV/71nj17yl+PGm43Rmm8oR4eDgfN2SgpDvm+RURERERqElTFxq+//so111zDjTfeSPv27dm4cSO33XYbRx11VIX1evfuzeOPP86JJ55IQkJCNXurWb9+/SoM/T74gqS+y0QilQGYB/25p8DNFe/9ws2nHMFFx3SoUCElIiIiItUbPHgw+fn5zJw5kwsuuIBXXnmFYcOGYbVaKSgowOl01umhp2OPPZY333yT9u3bExMTw1tvvcXAgQNp3Tp6KhPKBodD6IeHA5gxMZCXC0VKbIiIiIhI0woqsdGpUyeeeeaZ8tkafr+fo48+mjfffLPSugUFBfVOakDNFyT1XSYSaVrHOWgTZyc10cn5/doza1UG6fkueh4Wz9JtOTz+5e/8vDOHu886igRn0B3jRERERFosm83GAw88wKRJk3jsscfw+XxMnToVgDFjxnDnnXcyYsSIoPd32WWXsWfPHl566SWys7MZOHAgDz30UGOF3yjKBoebTic0xnVRrCo2RERERCQ8DNM8tONr7XJzc9mwYQODBw8OSRA9e/bkiy++KB/+vXDhQiZNmkR8fHz5BcmRRx7ZoGXB2rcvv0IPXIfDql5yEpS6niturx+71cAwDEzTxOMzsVsN3vt5F899vQWf36RjqxgeOa83vVMTGzFyCQe9t0iwdK5IXeh8kWCF6lwxDGjbNrI+p2RmZrJq1SqOPfZYUlJSwh1OWK8vrBvWk3LSYPwpKexftzXk+289fAi2dWvJmfYJnpNPCfn+Wzq9p0td6HyRYOlckbrQ+SLBCsf1Rb0SG02hpguS+i4LhhIbUl+hPFdWp+dx55y1pOe5sFsNbj6lOxcdk6bWVM2I3lskWDpXpC50vkiwmnNiI9KE8/rCtmI5rc8+DV+nzmT9/FvI95981inYf1lB7jsf4j7znJDvv6XTe7rUhc4XCZbOFakLnS8SrHBcX0Rsj5vU1FRSU1NDukwkWvTtkMTUCcdy/2cbWLxpP49/uYnlO3L519mB1lRrMvJ5/uvN3Di8O0e3180EEREREamsvBVVIwwOhwPDwykpaZT9i4iIiIhUxxLuAESkakkxdh4//2j+fmp3bBaDLzfu489TfmZtZj7z1mSybEcu89ZkhjtMEREREYlQZcPDzbi4xjlATEzgOKUJFBERERGRphKxFRsiAoZhcMmgTgxIS+K22WvYlVvCFe+uwFE6/HHB+r2M7pOKCSTH2umQFBPegEVEREQkYpRXbMQ1UsVGbCBhYqhiQ0RERESamBIbIlGgT4ck9hS4AfD6wesP9KzLLvIwYeqK8vV+mjQ8LPGJiIiISORp9FZUsaUVGyXFjbJ/EREREZHqqBWVSJS4/9yeWC1VDw+3WgzuP7dnE0ckIiIiIpGssVtRacaGiIiIiISLEhsiUWJk71QmX3JMlcsmX3IMI3unNm1AIiIiIhLZGrkVVfmMjWJVbIiIiIhI01JiQyQKHVq38f3W7LDEISIiIiKRyygsBBqxFVVpxYYSGyIiIiLS1JTYEIkireMctImz0zs1gTtG9CAlzg7Aa99v47f0vDBHJyIiIiKRxCgKJDZorOHhMZqxISIiIiLhoeHhIlEkNdHJ7KuGYLcaGIbBmL7tmTTrN77bks2kWWt4a/xAUhOd4Q5TRERERCJAow8PL5vdoRkbIiIiItLEVLEhEmUcNguGEWhGZbNaeGhUb45oG8f+Qje3zvyNYo8vzBGKiIiISCRo7OHhqGJDRERERMJEiQ2RKJfgtPHUBX1JjrWzbk8B//50PX7TDHdYIiIiIhJmRiMPDz8wY0MVGyIiIiLStJTYEGkG0lrF8PiYo7FZDL7cuI//fbct3CGJiIiISJg1eiuqsoqN4qJG2b+IiIiISHWU2BBpJo7p1Io7zzwSgDeWbueztXvCHJGIiIiIhNOBVlSNlNiILa3Y0IwNEREREWliSmyINCPn9W3PhOM6AfDA/PWsTs8Lc0QiIiIiEi6NXbFBaWJDw8NFREREpKkpsSHSzFx/cjdO7p6C22cyaeZvZOTpQlNERESkJTIKC4HGGx5ePmNDw8NFREREpIkpsSHSzFgtBg+M6kWPtvFkFXmYNPM3ij2+cIclIiIiIk3MKCpLbDT2jA09SCMiIiIiTUuJDZFmKN5h46mxfWgda2fD3kLumbcOv2mGOywRERERaUKNPzy8rBWVKjZEREREoprPh/3bJTinf4T92yXgi/yHpJXYEGmmOiTF8Pj5R2O3Gnz1+35e/nYrazLyufbDlazJyA93eCIiIiLSmNxujOJAwsG2elXjXJzGllVsKLEhIiIiEq0cc2aTMqgPyWNHkTTxSpLHjiJlUB8cc2aHO7QaKbEh0owN6NiKu848CoA3f9jBS99sYdmOXOatyQxzZCIiIiLSWBxzZpNyXL/yr1td9n+NcnFqxgZmd2jGhoiIiEh0csyZTdKVE7Ds3l3hdUt6OklXTojo5IYSGyLN3LGdWzG6TyoAP2zLAWDB+r2sy8xnbWY+6RouLiIiItJslF+cZqRXeL0xLk7LZ2x4PFHRrkBEREREDuLzkXD3bWCaGIcsMkpb2ifcfXvEfs6zhTsAEWlcY179sdJr2UUeJkxdUf71T5OGN2VIIiIiItIYark4NQ2DhLtvJ2vkKLBaG3y48hkbAMXFkJDQ4H2KiIiISNOwL/0O6yGVGgczTBPr7l3Yl36H58STmzCy4KhiQ6SZu//cnlgth17aBlgtBvef27OJIxIRERGRxlB2cVr1J7+KF6chUVqxAWCUqApYREREJJpYMjNCul5TU2JDpJkb2TuVyZccU+WyW087gpG9U5s2IBERERFpFE1+cWqxYDqdABjFRaHZp4iIiIg0CX9q+5Cu19SU2BBpQQ59eu+JL39n4fq9YYlFREREREIrHBenZmygHZUqNkRERASfD/u3S3BO/wj7t0sidjaDBHiGDsOXloZpVF3vaxoGvrSOeIYOa+LIgqPEhkgL0DrOQZs4O71TE7hjRA96pSbgsBr4TLhzzlqmray+n56IiIiIRIdwXJyWzdkwSopDtk8RERGJPo45s0kZ1IfksaNImnglyWNHkTKoD445s8MdmlTHaqXgwccAMA9ZVPZ5suDBR0Mym60xKLEh0gKkJjqZfdUQJo8fyLgBabw9fiALrxvGHwZ0wAT+s/B3Xvt+G6Z56NuYiIiIiESNgy9OD0luNNrFadmcjWJVbIiIiLRUjjmzSbpyApZDBlFb0tNJunKCkhsRzD16DHmvTwGHo8Lr/g5p5L0+BffoMWGKrHZKbIi0EA6bBaP0gtYwDGIdVm4/owd/HXo4AP/7bhtPLtqEX8kNERERkahVdnHq79ChwuuNdXF6oBWVKjZERERaJJ+PhLtvA9Os1ALdKL3HlHD37WpLFcHco84r/0xXcOc95MyYS9by1RGd1ACwhTsAEQkfwzC45sSuJMfaeWLRJj5YsZucYg/3ntMTu1V5TxEREZFo5B49hqyRo7Av/Q5LZgb+1PaB9lON0EbALK3YMIqV2BAREWmJ7Eu/w7q7+hbnhmli3b0L+9Lv8Jx4chNGJsGy7NyBJTcX02aj+NobwekMd0hBUWJDRLj42I4kx9q597P1zF+3l9wSL4+NOZpYe2T20BMRERGRWlitTXLzwIyNA1SxISIi0lJZMjNCup40PduqXwHw9ewdNUkNUCsqESl1du92PHVBH2JsFpZuzeb6j34lt9gT7rBEREREJJKVz9hQYkNERKQl8qe2D+l60vRsq1YC4O3XP8yR1I0SGyJSbli3FF68qD9JMTZWpedz1Qcrycx3sSYjn2s/XMmajPxwhygiIiIiEcSMKZuxoeHhIiIiLZFn6DB8aWlUN7HVNAx8aR0DbTElItlWByo2lNgQkajWPy2JVy4eQLsEB1v2F/HX937h/Z93sWxHLvPWZIY7PBERERGJIOUzNtSKSkREpGWyWil48LEqF5lGYJx4wYOPNsqsLwmNslZU3n4DwhxJ3SixISKVHNE2nodH96Z9opOMfBefrd0DwIL1e1mXmc/azHzS8/RUnoiIiEhLZ8aWVmyoFZWIiEiL5R45CjMpqdLr/vbtyXt9Cu7RY8IQlQTD2L8f6+5dAHj79A1zNHWj4eEiUqW/vr+y/P/LygmzizxMmLqi/PWfJg1v4qhEREREJKKUzdhQxYaIiEiLZf/+Wyx5efhbtSLv1bdIvPk6rLt3U3jPA0pqRLjy+RrdumMmVk5ORTJVbIhIle4/tydWi1HlMqthcP+5PZs4IhERERGJNGZsHABGsap5RUREWirnzOkAuEafj+fU03GN+yMAjoULwhmWBCFa21CBEhsiUo2RvVOZfMkxVS5LirHSLsHZtAGJiIiISMTRjA0REZEWzuvFOXcWAK7zxwHgPvNsAByLFoLPF7bQpHa236JzcDgosSEiQTAO+TO72Mu1H/7K819vwePzhyssEREREQkzM6Z0xkaJKjZERERaIvuSxVj278ffti2ekwItyz2Dh+BvlYwlKwvb8mVhjlBqUlax4evbL8yR1J0SGyJSrdZxDtrE2emdmsAdI3rQOzWBlDg7Z/U6DBN4+6cdXPHuL2zNKgp3qCIiIiISDrGlMzY0PFxERKRFcs4qbUM16nywlY5zttlwn34GAI6F88MVmtSmsBDr7xsB8PSNvlZUGh4uItVKTXQy+6oh2K0GhmEwtn8HPD4Th83CGUcdxsMLNrBuTwF/nvIzt5zanbH9O2AYVc/lEBEREZHm50DFhhIbIiIiLY7bjXPuJwC4LhhXcdGIs4mZMQ3ngs8ouvOecEQntbCtWY1hmvjapWKmpoY7nDpTxYaI1Mhhs5QnKwzDwGELvG2cfmRb3r10EMcfnozL6+eRhb9z66w1ZBe5wxmuiIiIiDQhM7Y0saHh4SIiIi2OY/GXWHJz8LVLxTN0WIVl7tPPxDQMbGtWY9m1M0wRSk0ODA6PvvkaoMSGiDRAu0Qnz1/Yj5tP6Y7davD1pv3839s/8/3WLADWZORz7YcrWZORH+ZIRURERKQxqGJDRESk5XLOLG1DNeYCsForLDPbtMF73PEAOBYuaOrQJAi21WWJjehrQwVKbIhIA1kMg/HHdWLyJQPp1iaO/YVu/jZtNU8t2sQnqzNYtiOXeWsywx2miIiIiDSGshkbGh4uIiLSspSU4Ph0LgCu8/9Q5SruM88GwPH5Z00WlgRPFRsiIsBR7RJ4e/xARh3dDoD3ft7FjF/TAViwfi/rMvNZm5lPep4uekVERESaC1VsiIiItEyOLxdiKcjHl9YR7+Djq1zHNaI0sbFkMRTrs0JE8Xiwrf0NAG/f6ExsaHi4iIRMjN3K3DV7yr/2mYE/s4s8TJi6ovz1nyYNb+rQRERERKQRmDGBig1DNytERERaFOesaQC4xowFS9XPzvv69MWX1hHr7l04vluC+4yzmjJEqYF1w3oMtxt/YhL+Ll3DHU69qGJDRELq/nN7YrUY1S4/pmMSP23Pxm+aTRiViIiIiDQGMzYOUGJDRESkRSkqwjk/0F7KdcG46tczDNxlVRufz2+KyCRItlUrAfD27VdtYirSRWfUIhKxRvZOZfIlx1S7/JddeVz30SrGvPojL32zha1ZRZXW0dBxERERkehQVrGhGRsiIiIth2PhfIyiQnyHd8U7cFCN6x6YszEf9JBrxDgwODw621CBEhsi0oiMQ/7811lHMrZ/exKcVjLzXbz5ww4uenMZf3l3BR//spvcYg8A89Zkaui4iIiI1NmGDRv4wx/+wODBg3n00Ucxg7x43rZtG8cfX3VvaIC///3vPPDAA6EKs3mJ1YwNERGRliZm5nQAXOePBaP6rh0A7pOGYzqdWHdsx7p+XVOEJ0EoHxwepfM1IIITGzNnzuTUU09l4MCBXH755ezcuROo+WKlvhcyIhJareMctImz0zs1gTtG9KB3agJt4uwM6ZrCnWcexWcTT+Dh0b05sVsKVgNWp+fz6Be/c87L33Pdh78y97dAQkNDx0VERCRYbrebiRMn0qdPH6ZNm8amTZuYPn16rdvt2LGDq6++mtzc3CqXL1myhKVLl3LTTTeFOuRmoXzGhtcLHk+YoxEREZHGZhTk41gYaCtVYxuqMvHxuE8KzFpVO6oI4fdjW70KAG+/AWEOpv4iMrGxfft2nnnmGV588UXmzp1LWload9xxR40XK/W9kBGR0EtNdDL7qiFMHj+QcQPSmDx+ILOvGkJqohMAp83CmT0P45lxfZlzzVBuPqU7AF4//LQjhwK3DzgwdPzSqSsY8+qPYft+REREJPJ9/fXXFBQUcMcdd3D44Ydzyy238PHHH9e63TXXXMNFF11U5bKSkhLuu+8+Jk2aRFJSUqhDbhbMmNjy/1fVhoiISPPnmP8pRkkJ3u5HBP20/4E5G581ZmgSJMu2rVjy8zCdTnxH9Qx3OPUWkYmNNWvWMGDAAPr06UNaWhrjxo1jy5YtNV6s1PdCRkQah8NmwSgtRzQMA4et6rebtvEOxh/XifvP7Ul1M8etFoP7z43eN1oRERFpfOvWrWPAgAHElrZG6tmzJ5s2bap1u//973+cc845VS576aWXKCkpwWaz8f3336sivCplMzYAilVhKyIi0tw5Z5W2obpgXK1tqMqUzdmw//QDRk52o8UmwSmfr9HraLDbwxxN/UVkYqNHjx4sXbqUNWvWkJ+fz7vvvsuJJ55Y48VKfS9kRCQyjOydylvjB1a57LU/DWBk79QmjkhERESiSUFBAZ06dSr/2jAMLBZLtS2mynTu3LnK13fv3s2bb75Jly5d2L17N48//jg33HCDkhuHMgxMzdmQ5sjnw/7tEpzTP8L+7RLw+cIdkYhI2Bm5OTi+XAiA6/w/BL2d//AueHv2wvD5cCz6orHCkyA1h8HhALZwB1CVHj16cPbZZzN27FgAOnXqxEcffcQrr7xS7cVKTRcyrVq1CvrYdru1wtc2m7WaNUUq0rnScGW/fwZw8C2DN37YwfMX9SuvAGkOdL5IsHSuSF3ofJFgNcdzxWq14nA4KrzmdDopKSmp0/VAmenTp9O2bVvefPNNHA4Hl156KaeffjrffvstJ510UtD7aQnXF2ZMLEZxMQ6fG7+j+X1/4dIcz5VoYZs9i9h//gPL7l3lr/nTOlL8n8fxjjk/jJFVT+eLBEvnitTFoeeL/fNPMdxufL16Yx3Qj7qcTb5zRmJbv46YL+ZjXnxxaAL0+bB+9y2WzAz8qe3xDTsRrDrHa+P4LTBfg2OOwRGiz27heG+JyMTGL7/8wqJFi/joo4844ogjeOWVV7jqqqsYOnRotRcrobqQ8Xh8HPoQltutJzMkODpXGibBbqVNnJ3URCfn92vP1GW72JFTzJJN+3n+q81MPLFruEMMKZ0vEiydK1IXOl8kWKE4VyLpmYNWrVqxcePGCq8VFhZir2d5fWZmZoXrj4SEBLp06cLOnTvrtJ+WcH1RNkDcm1+It5l9b+HW3M6VaOCYM5u4Kydw6C+ukb6buMvGk/f6FNyjx4QpuprpfJFg6VyRujj4fImZFmj7XzJmbJ3PI/P0s3A++zS2hZ/jLnY3OAHhmDObhLtvw7p7d/lrvrQ0Ch58LGLfpyNFwsqVAJT06hvSz25NfX0Rka2o5s2bx6hRo+jfvz/x8fHcfPPN7Ny5k1atWpGVlVVh3bKLlZqWiUh0OHTo+LQrjuO2M44A4PWl25m1Kj3MEYqIiEik6tevHytLL9IAdu7cidvtrle1BkD79u1xuVzlX/v9fjIyMkhLS2twrM1NWWKDIrWikijn85Fw921gmhx6X8UoTXQk3H272lKJSItkZO3HsXgRAK4Lgm9DVcYzeAj+VslYsrKwLV/WoFgcc2aTdOUELAclNQAs6ekkXTkBx5zZDdp/c2bs2YM1MwPTMPAe3Sfc4TRIRCY2fD4f+/btK/+6sLCQoqIibDZbtRcrob6QEZHwOHTo+EXHdOSKIYHe1498vpHvtmTVtLmIiIi0UIMHDyY/P5+ZM2cC8MorrzBs2DCsVisFBQV4PJ467W/kyJEsWrSI+fPnk5GRwZNPPonb7ebYY49thOijXIxmbEjzYF/6HdbduyslNcoYpol19y7sS79r0rhERCKBc94cDK8XT9/++HocWfcd2Gy4TzsdAMfC+fUPREnoBrGtDtw/9x3RAxISwhxNw0RkYuPYY4/l888/Z/LkyXzyySdcd911tG3blgkTJlR7sVLThYyIRLeJJ3bl3KPb4TPhjk/Wsj6zINwhiYiISISx2Ww88MAD3HvvvQwbNoz58+czadIkAMaMGcPixYvrtL/u3bvz9NNP89///pezzjqLxYsX89JLL5EQ5ReAjcGMK0tslIQ5EpGGsWRmhHQ9EZHmxDlzOgCuC8bVex/uM88J7GvBZ/Xeh5LQDWNb1TwGh0OEztg499xz2bJlC2+99RZ79+7lyCOP5Pnnn8dut/PAAw8wadIkHnvsMXw+H1OnTgUOXMhUtUxEopthGNx91lHsLXDz0/YcbpqxmjcvOYYOSTEh2f+ajHye/3ozNw7vztHtE0OyTxEREWl6I0aMYMGCBaxatYpjjz2WlJQUAL788ssat+vUqRPr16+v9Pqpp57Kqaee2hihNiumKjakmfCntg/peiIizYWxdy/2bwIPibjGjK33ftynn4lpGNjWrMayayf+jp3qvA8loRumPLHRd0CYI2m4iExsGIbBDTfcwA033FBpWXUXK7UtE5HoZrdaeGzM0Vz1/kp+31fITdNW89r/DSAppuFzdOatyWTZjlzmrclUYkNERCTKpaamkpqaGu4wWpTyGRuq2JAo5xk6DF9aGpb09PJ2JgczDQN/hzQ8Q4eFIToRkfBxzpmF4ffjGXgs/q7d6r0fs00bvIMGY1/2I46FCyi57Io670NJ6IaxrQq0omoOFRsR2YqqNqmpqYwYMaLKxEVNy0QkuiU4bTwzri/tEhxsySriH7PW4Pb667Wv9LwS1mbmsy4znwXr9gKwYP1e1mXmszYzn/Q8XZiLiIiIBKWsYqOoKMyBiDSQ1UrBg48BcGhao+zrggcfBbW8FpEWxjmrtA3V+XUfGn4o91mBdlSOz+vXjsozdBj+uLhql5uGgS+to5LQVTDy87Bt2QyAt1/0V2xEZWJDRFqu1EQnz4zrS7zDys87c7l//nr8VTxNVZsxr/7IpVNXMGHqCrKLA8NEs4s8TJi6gkunrmDMqz+GOnQRERGRZqmsYkMzNqQ5cI8eQ97rU8B2SGW400ne61Nwjx4TnsBERMLEkpGO/ftvAXCdX/82VGVcI84GwLFkMRTXvY2lY8FnWEofpqiUhDYCkzeUhK6a7bfVAPjSOmK2aRPmaBpOiQ0RiTpHHpbAo+cdjdViMH/dXl5csjXobX1+k5935jC0a+tq17FaDO4/t2cIIhURERFp/szSpyY1Y0OaC/fIUWAN3C4pvGlS6YtuPIOHhDEqEZHwcH4yE8M08QweUq+ZGIfy9emLL60jRnExju+W1GlbIzOTxFsCowtcZ5+LPy2twnL/Ye2UhK5Bc2pDBUpsiEiUGtK1NXefdSQAb/+0g49/2c2ajHyu/XAlazLyK6zr8fn5bksWDy3YwMiXl3LNB7+ydGt2tfv+19lHMbK3enOLiIiIBEMzNqS5sezYjuFyYTqdFP3zbjyDjsMwTZxzZoY7NBGRJuecWdqG6oJxodmhYeA+4ywAHJ/PD3470yTppmux7N+Pp29/8l57i6zlv5EzYy6e0hv1JZf+RUmNGhwYHK7EhohIWI3u055rhnUB4PEvf+fV77eVDwEv8fhYtHEf/5q3jrP++z03TV/NzFUZZBd7SIqxMapPKjef0h0A45D9Pjh/PR/9shuzHi2uRERERFqcshkb9WgnIRKJbBvWAeA74kiwWnFdEOgpHzNjWjjDEhFpcsbOndh/+gHTMHCdd0HI9ntgzsZ8CPLeS8zr/8Px5ULMmBjy//saOJ1gteI58WSKr7oWAOe8OSGLsTkqT2w0g/kaALZwByAi0hDnHt2OdXsKWPz7fr7ZnAXA9F/Tmf5rOh7fgX8c28Q7OLVHG047si2DOrXCZrWQme9iyk87SE10cn6/9kz/NZ3N+4vw+Ewe++J3ftmZy51nHUm8Q2+VIiIiItXRjA1pbqwbNgDg7RloT+saM5b4e+7E/uNSLDt34O/UOZzhiYg0GXtpQtdzwon423cI2X7dJw3HdDqx7tiOdf06fL1617i+de0aEu77FwAF9z6Ar2evivs7eySmzYZtzWqsm3/H171HyGJtNlwurOvXAs2nFZXu1olIVDv/tZ8qvXZwQgPgtT8NoF9aEhajYm1GaqKT2VcNwW41MAyDsf074Pb6+XhlOs8v2cKC9XtZv6eA/4w5mh5t4xv1+xARERGJVmZ5xUZRmCMRCY3yio0jA4kNf4c0PCeciOO7b3DOmkHx9X8LZ3giIo3P58O+9Dscr78KENJqDQDi4/GceDKOLxfi+Hw+xTUlNlwukq79K4bLhWvEWZRccXWlVczWKYH9LV6EY85siv92S2jjbQZsG9ZheL34k5ObTYJerahEJKrdf25PrJZDm0kFlA0BH9CxVaWkRhmHzYJRuswwDJx2K+OP68T//tifdgkOtmUXc/k7K5i3JrPRvgcRERGRaGbGBRIbmrEhzYV143oAvAc9EVzWjso5U+2oRKR5c8yZTcqgPiSPHYV16xYA4p55Asec2SE9juvMswPH+/yzGteLf+g+bGtW42/blvxnXoJq7u+4Rp8PgHPOrJDG2VxUaENVzc8w2iixISJRbWTvVCZfckyVyyZfcky9h4AP6NiKqROOZWiX1ri8fu79dD0PLdiAy+tvQLQiIiIizVBZxUaJZmxIM2CaWNcHEhtlFRsQuGFmWq3YV67Auvn3cEUnItKoHHNmk3TlBCy7d1d43bInk6QrJ4Q0ueEeEUhs2H/6ASMnu8p17IsXEffyCwDkP/MiZrt21e7PNXI0pmFg/2UFlh3bQxZnc2FbtRJoPoPDQYkNEWlGjEP+bKjWcQ6eGdeXq4d1wQBmrsrgindXsDMncNG+JiOfaz9cyZqM/BAdUURERCT6lM/YKFbFhkQ/S/puLAX5mFYrvu5HlL9utm2LZ/ipADhnTg9TdCIS9Xw+7N8uwTn9I+zfLgGfL9wRHeDzkXD3bWCale6rGKUDvhPuvj1kMfu7dMXbsxeGz4dj0ReVlhtZ+0m8cSIAxZddifuskTXuz2zXDs/QYQA454a2uqQ5OFCxocSGiEjEaB3noE2cnd6pCdwxoge9UxNoE2endZyjwfu2WgyuOqELz/+hH8mxdjbsLWTC1J9ZtHEf89ZksmxHrtpUiYiISItmqmJDmhHrhtJqje5HgKPi9UTJ2AsBtaMSkfo5uMVT0sQrSR47ipRBfULe4qm+7Eu/w7p7d7UPixqmiXX3LuxLvwvZMcuqNhwLDmlHZZokTroJa0Y63h5HUnDfQ8Htb/QYAJwR8jONGH4/1t9WA6WtqJoJJTZEJOqVDQGfPH4g4wakMXn8QGZfNYTURGfIjjGka2vemXAsvdslUODycdvsNcxclQHAgvV7WZeZz9rMfNLzan9ScU1GPn99d4UqPURERKRZMGMDFRsUK7Eh0e/QweEHc48chelwYFu3FuvaNU0dmohEsWpbPKWnh7zFU31ZMjNCul4w3GedA4Bj0cIKlSAx703FOXc2pt1O/suvQ1xcUPtzjQokNmw//RDSOKOddcsmLIUFmDEx+I7oEe5wQkaJDRFpFg4dAu6whf7trV2ik7V7Csq/Lpu3kV3kYcLUFVw6dQVjXv2RrfuL8Piqn8Uxb00mP23LUaWHiIiINA+xgZsNqtiQ5sC6YQMA3p6VExtmq2Tcp58JgHPmx00al4hEsSZu8VRf/tT2IV0vGJ7BQ/C3SsaSlYVt+TIALJs3kXDnbQAU3n433v7HBL0/f1pHPIOOwzBNHHM/CVmc0a68DdXRfcBmC3M0oaPEhohIHdx/bk+slpqneFw0eRknPfsNY1//kZumr+LJRZt4/fttfPzLbr7ZvJ8F6/YCda/0EBEREYlE5TM2SvR5RqKftYaKDQDX2D8AEDNjGpTekBQRqUk4WjzVh2foMHwd0qjunc00DHxpHcvnWISEzYb7tNMBiHnzVZwfvU+rSy/GKCrEPewkiq//W5136Rp1PqA5GwcrT2z0bT5tqACaT4pGRKQJjOydSreUOCZMXVFp2dAuyeQUe9meXUyRx8fOnBJ25pTw3ZbsKvdVVulR5qdJwxstbhEREZHGohkb0pzYNpbO2OjZq8rlrrNGYsbFYd26BdvKFXiPObYpwxORaOP14pg5PahVw946yWrFPeIsYqdMxoQKiRiztENGwYOPgtUa0sP62x4GQOy0D4md9mH58VxjL6zXsVyjx5Bw/7+wf/cNxv79mG3ahDTeaGRbtRJoXoPDQYkNEZF6M6D8H3sTuP7kbvRKTcQ0TfYVutmeXcy27GK2ZxWzbHs26/cWVrkfq8Xg3nOOasLIRUREREKnrGKDYlVsSHQz9u3Dsn8/pmHgPeLIqleKj8d11jnEzJyOc8Y0JTZEpGp+P86Z04h74j/Yft8Y3CYhbPFUH0Z2VnmVg9mqFUZubvkyf4c0Ch58tHw4d6g45swm9vVXKiVSME0Sbvs7/jZt63xMf9duePr2x776V5yfzaVk/KWhDDn6mCa21aUVG0psiIi0bK3jHLSJs5Oa6OT8fu2ZtSqDzHwXreMcQGDGx2EJTg5LcDKoc3L5dusy86us9OicHEO3lOAGYYmIiIhEnNjSio3iokBrHqPmtp0ikapscLi/c5caB9W6LrgwkNiYNZ3Cex8Ai7p8i7Q4Ph/2pd9hyczAn9o+0J7JagW/H8fcT4h//GFs69YC4G/dGrw+jIL88pkaBzMNA3+HtNC2eKqHuCf+gyUrC2/vo8lesBj7sh9x7N+Du027A99fKNU0e4TAA6QJd99O1shRdT62+7zzsa/+FcecWS0+sWHJSMeybx+m1Yq3d59whxNSSmyIiNRRaqKT2VcNwW41MAyDsf074PGZQQ8sL/sHuszWrGIue2cFfzq2I9cM60qcI8QfFprYmox8nv96MzcO787R7RPDHY6IiIg0MrMsseH3g8cDDkeYIxKpH+uGQBuqqgaHH8x9+gj8iUlYd+/C9uMPeIee0BThiUiEcMyZTcLdt2Hdvbv8NV+HNEou/BOORQuxlz4d72+VTPG1N1B89bXYv1pE0pUTMA2jQnKj7P8ao8VTXVjXryP2jVcDsTzwH3A68Zx4MobDisfdOEPNy2aPVOfg2SOeE0+u075do88n/pEHcHz9FUZuDmar5AZGG73K2lD5jjyq/GGU5kKPFYiI1IPDZsEofRrRMIygkhpllR69UxO4+5yjODo1gdaxdoZ3T8FvwrvLd3HRmz+x+Pd9jR1+o5q3JpNlO3KZtyYz3KGIiIhIEyibsQGasyHRrbbB4eViYnCPHBX431nTGjssaUo+H/Zvl+Cc/hH2b5eAr3Fu6Er0csyZTdKVE7AcckPekr6b+Oefwr76V/wJiRROup2sZb9SdMttmAmJuEePIe/1Kfg7dKi4w9hY8l6fEvIWT3VimiTcfTuGz4fr3PPwDD+1SQ4b7EyR+swe8R15FN6evTA8HhwLPqvz9s3JgcHhzasNFahiQ0SkyRxc6eF02hhzdGp5pcf3W7P4z8Lf2Z1bwq2z1nBqjzZMOu0I2ifFhDvsoKTnlZBT7MEAFqzbC8CC9XsZ3ScVE0iOtdMhSr4XERERqSOH48ATqMUlkNQq3BGJ1IttwwYAvNUMDj+Ya+wfiPnwPZyzZgSebrbp9kq0q/Ip/LQ0Ch58LLw3nSVyBNE6yUxIJOuHFZiHtau0uXv0GLJGjsK+9DtsPy4l4ZEHAq+fdkajh14Tx/xPcSxehOlwUPDvB5vsuMHOFKnv7BHXqDHY1q/DOWc2rov+VK99NAfliY1+A8IcSeipYkNEpAlVV+lxQtcUPrhsEJcf3xmrxeCr3/dz8eTlvLt8J15/oDh1TUY+1364kjUZ+WGLvzpjXv2RS6euYMLUFWQXewDILvIwYeoKLp26gjGv/hjmCEVERKTRGMaBORuq2JAodqBi46ha13UPPw1/69ZY9u3F/t03jR2aNLLqn8JPJ+nKCTjmzA5TZNLo6lClY/92CdbduyslNcoYgKUgH1tpW7sqWa14TjyZ4ptvxXd4V4ziYhxfLGjQt9AgLhcJ99wBQPG1N+Lv2q3JDu0ZOgxfWhpmNbO5TMPAl9ax3rNHXKPPB8CxaCEUFNQ7zmjXXAeHgxIbIiIRI8Zu5fqTuzF1wrEMSEuiyOPj6a8285d3VrAmIz+iWzz9bXj1H36sFoP7z62lnF9ERESimhkTqMw0ipXYkOhk5OVizUgHwHdUEJ9d7XZcoy8AwDlT7aiiWk1P4ZfOQki4+3a1pWqGHHNmkzKoD8ljR5E08UqSx44iZVCfioksrxf711+RMOkmki6/JKj9BtU6yTBwnT8WgJhZM+oTfkjEvvJfrFu34EttT9FNtzTtwa1WCh58DKBScqPs64bMHvH16YuvazeMkhIcX37esFijlJGbg3X7NgC8ffuFOZrQU2JDRCTC9Ggbzyt/GsCdZx5JvMPKuj0FXP7OCmauCnw4WrB+L+sy81mbmU96XklYY/X5TaYu28n/vttW7TqTLzmGkb1TmzAqERERaWplczZUsSHRqmxwuK9DGmaQ7dRcY/8AgHPOLHC7Gy02aVxlA4yrfQr/oAHG0nzUVqUT9/gjJNx6M236H0XyhWOInfImliCf+g+2dVJZYsOxcH5YKgqMzEzingokFgrv/jdmQmKTx1Dd7BF/h7SGzx4xjPKqDeecWQ0JM2rZVq8CwHd4F8zk1mGOJvSU2BARiUAWw2Bs/w4UugNPBZmAy+sHIqfF09asIq56fyXPLt6My+unX/vAh6BDLwh25oY3+SIiIiKNzyxvRaV/9yU6lbWOqXVw+EE8J5yIr10qlpwcHIu/bKzQpJE15gBjiVC1VemYJvGPP0Ls229g2bcPf0oKxRMuJ+f9aYHkZ4haJ3n7DcDbrTtGcTHOhfMb+E3VXfzD92EpLMBz7KCwzqBwjx5D1vLfyJkxl7yXXydnxlyylq8OyWwbV+k+HJ8vgBZYVWpbtRJonoPDQYkNEZGIdv+5PbFaqv7QZDHg/pFN3+KprErjz1N+ZlV6HvEOK3edeSQPn9ebNnF2eqcmcOvpRxBrD/wT8/SiTewr1BNsIiIizVppxUZLvGkgzUNZxYa3Zx0+X1ut5U9cO2eoHVW0auwBxlGvDjMookWtVTqlf7pGnE3OBzPYv2ojBU8+h+f0Myl4KIStkwwD1/njAHA2cTsq24rlxL43FSiN2RLmW8Sls0dc4y7Cc+LJ9W4/dSjvwEH4OnbCUliA46swJaDD+Dt0YHC4EhsiItLERvZOZfIlx1S5zG/CrNUZrM1sumHiW/cXcdX7v5RXaQzt0pr3LxvEBf070D4phtlXDWHy+IFcPLAjs/56PJ2TY9hT4OaWGasp9kT/B2ARERGpWvmMDVVsSJQ6MDi8bg8OuS4ItKNyfDpXib0oVT7AuJrlDR1gHM2CmkERhYKtvnFd+Ec8p50Bdnv5a6FuneQaU9qO6osFGAVNdG1vmiTcdTsAJRf9Ce9xxzfNccPBMHCNOg8ITzuqcP8ONefB4aDEhohI1DAO+dNmgeU7crl06gr+NW9dyOdtrMnI59oPV7ImIx+f32TKTzsYP2U5q9LziXdYufusI3nuD31pnxRTvo3DZsEofVKldZyDZ8f1o1WMjbWZBdw9dx0+f3WXCyIiIhLNDrSi0o1diU62DRsA8PXsVaftvMcdj6/z4YGngRcuaIzQpLEdNMD4UKEYYBytaptBEc3JjYZW6YSydZKvT1+8R/QIDLie/2mdt68P5/SPsC/7ETMunsJ/3dckxwwnd+mcDcf8T5t0HlLYf4eKiw9UI/Yb0LjHChMlNkREIlzrOEd5i6c7RvSgd2oCbeLsvHLxMYzs3Q6Az9bu4cI3fuL5rzeTX+INyXHnrclk2Y5c3v95F1e9/wvPfb0Ft89kaNdAlcb5/TqUJzGq07l1LE9e0AeH1eDrTft5+qtNIYlNREREIkt5xYaeWJdoVFiIZcc2ALxH1S2xcXArmZiZakcVrdxnj8SMi6v0ekgGGEej2mZQAAl33x61bak8Q4fha5fasCqdULVOMowDLe2aoh1VYSHx998T+N+/34q/fYdaNoh+nsFD8B/WDkteLvZvFjfNQSPgd8i2bg2Gz4e/TZtm+/esxIaISIRLTXSWt3gaNyCNyeMHMvuqIfRLS+L+c3vx9p8HMqhzK9w+k7d/2snY13/k/Z934fEFho0fXHlRm/S8EtZm5rMuM58F6/YC8OnaPaxKzyfGZuGGk7vy3LiKVRq1GdCxFfeNDFwgfrBiN+/9vKsePwURERGJaLGlNwSV2JAoZNu0EcM08bdpg9mmTZ23d40tbUf1+WdN10pGQsr+w/dYiorwt25N7suvA2BaLGQv+rblJTUIYgaFaWLdvQv70u+aNK6QKSkBpxMDKiU3wlGl4xoTSI46vvwcIz+vUY8V9/xTWNN34zu8K8XXXN+ox4oYViuuc8vaUTVNpVEk/A6Vz9fo2x9qeSg1WimxISISBQ5u8WQYBg7bgbfv3qmJ/Pei/jx1QR+6psSSW+LlyUWbuHjyMr7csJe5vwUqL+atyaywT5/fJLvIzaZ9hSzbnsOCdXsY8+qPXDp1BROmriC72FNh/RKvnxeWbK21SqMqI3oexo0ndwMCw8QX/76vzvsQERGRyKUZGxLNylt11LVao5S3b/8DrWQ+mxfK0JqPCB9A7Zgf+HtznzUS97iL8HY/AsPvx/7D0jBHFh7BzqAIdr2IYpok/v16rDu2409MqtRuKhxVOr7eR+M98igMt7tR30Ms27cR9+JzABTc9xDEBP/AYrRzlf59Oj+dA97QdLmoSST8Dh0YHN4821AB2MIdgIiINJxhGJx8RBtO6JbC7FXpvPTNVnbklHD7J2uxWgKJiBm/prMqPY+8Ei/5JV7yXV7qMvLCajG495yj6h3jhMGd2JVbwvRf07lr7jr+d/EA+rRPrPf+REREJHKYMZqxIdGrLLFR18Hh5UrbUdmeegznzGm4Lrw4hNFFP8ec2STcfRvWg/rM+9LSKHjwsciohjBNnJ8Gbia7zhkFgGf4qdg2b8L+9SLc55wbzujCwn9Yu+DWC3JWRZ34fNiXfoclMwN/avtAO6gQVk7EvvgcMTOnY9ps5L3zIZ7BQxr1eEExDFxjxmJ78lGcs2fguuhPodv3QT/PmLfewHC5cJ98Cu5zR4fuGFHAM+wk/K1bY9m/H/vS7/CcNLxRj9fQOS6hYFu9Emi+g8NBFRsiIs2KzWIwbkAauQfN2Sgb2O32mazJKGBnTgm5JQeSGq1ibHRNiWVgp1accVRbzjiybZX7nnzJMYzsnVrv2AzD4B9n9OCErq1xef3cMmM1u3P1VKeIiEhzYMaqYkOil60ssdGznokNwDX2QgAci77AyM4KSVzNQdiH5wbBum4t1u1bMZ1O3KecBoB7eOBPx9dfhTGy8DCys4j934s1rhPUDIp6cMyZTcqgPiSPHUXSxCtJHjuKlEF9Qnae2L/6kvgH7wUCrabKkhghmZXRQGWzehyLvsDIzQnJPg/9eTq+/xYTcJ15TrNtTVQtu708cemcM6vRD+cZOgx/YvUPcjbW7xAQSGYtWYzt19LExtF9Q3+MCKHEhohIM3T/uT3LKzUOZTHguhO78uk1Q/j+5pNYeP0wPvrLYF65eAD/Oe9oLh/SGaC8F2QoP+7YLAaPnNebIw+LJ6vIw83TV4ds2LmIiIiEUWnFBsVF4Y1DpB6sG9YB4K1vxQbg69kLb+8+GB4Pcc89FbEtl5pUBAzPDYbzs7kAuIefCgkJAHhOOhnTMLBtWI8lfXcNWzcvtp+X0XrEcJwLPsO02TA5MHOiAtMM+QyKxk6CWbZuIenqyzH8foovmUDJX/7aoP2Fmq9Xb7w9e4WsHVV1P0+AhHvvjIikYlMrqxBzzP0E/P5GPZbzw/ew5AdmLlXZKKMRfofgoGTWH87D8ATai7f64wXN9u9biQ0RkWZoZO9UJl9yTJXL3ho/kL8MPZy2CU5s1sr/DLSOc9Amzk7v1ATuGNGD3qkJtImz0zrOEZLY4h02nh7bl3YJDrZkFXHb7N/w+Px1GnIuIiIikcWMLW1FVayKDYkybjfWLZuBQHKiIby9jwYg7sXnGuVp82gTCcNzg1E+X+PsAy2nzOTWeI8ZCIC9JVRtmCYxr71M8nlnY92xHV/XbuTMX0TeG1Pxd+hQeX2rFV+v3qE7fmMnwQoLaXXZJVhycvAcO4iC/zwZkRULZVUbztkzGrajmn6epX9GQlKxqbmHn4Y/MQlrZga2ZT812nEcXywg8ZYbAXCdex7+tLQq1/N17Rba41aXHMyInAq5UFNiQ0Skmatr5UVqopPZVw1h8viBjBuQxuTxA5l91RBSE50hiyk10cnTY/sSZ7eybEcuD32+sdoh5yIiIhL5TEfgc4J1wzo9pS5Rxbp5E4bPFxgi3L6KG7hBcsyZjXP6x5Vej6SWS00tEobnBnNs+8/LAXCfPbLCMk8LaUdl5OeReNXlJN55G4bHg2v0+WQv/BpvvwG4R48ha/lv5MyYS97Lr5MzfQ6uM87E8PlIuPMfYNZhaGMNGjUJZpok3nw9trW/4T+sHXlvvhOxQ7NdY8YC4PjqS4yc7HrvJ1qSik3O6cR95tmB/22kdlS2FctJuvJSDJ+Pkj/+H3lvTq34OzRjLiXnj8UAEkrbooVElFTIhZoSGyIizVRDKi8cNgtG6RMshmHgsIX+n4uj2iVw6xlHYAHm/pbJ7N8CFzQL1u9lXWY+azPzSc/TU58iIiKRzjFnNnHPPRn4/x++b/FPqUt0KWtD5TvqqPo/wV12Q6mKhiPN+YZSbSJheG5tHPM/BcBz7KBKcbiHnwqUVmyE6AZ+WPl82L9dgv3jD8sT0NbVq0geMZyY2TMw7XYKHnqUvNffxkxqdWC7g2dQnDScgocew3Q4cHz1Zcje5xszCRb7wrPEzAoMC899fQr+DlU/PR8JfEf1LG9p15B2VNGQVAwX1+jzAXDOnR3y32vL5k20Gn8RRlER7tPOIP/pFwL/rhwyx6Xwznsx7XYcXy7EvmRxSI7dUpNZtnAHICIijaOs8sJuNTAMg7H9O+DxmY2SpKiv+z/bUP7/JZ5Aj8vsIg8Tpq4of/2nScObPC4REREJTlnbg0NvDpQ9pZ73+pTyntYikahscLj3qPq3oSq7oVSdg28oeU48ud7HiTaeocPwpaVhqeFmm0ng5+cZcgLYmv4WVXkbqtKhwgfzDB6CGRODNTMD64b1DW5VFk6OObNJuPu28vM0DvC3SsYoLMDwevF16kzeq5PxDhpc67783Y+g6IabiH/qcRLuuYOs00dAfHyD4gs2uWXZtCnw702QSUj7lwuJf+jfABQ89BjeoSfUN8Qm4zp/LLa1vxEzcxquP42v1z6iIakYLu7TR2DGxWHdsZ2YKW9iJiTiT21fPki+vow9e0i+eCyWffvwDBhI7utTwG6vcl1/t+4UX3YFca/9j/j77yFn/iKwNOw+TUtNZkXO3S0REQm5pqi8aIiahpxbLQb3n1v/AY4iIiLSyFpo2wNpXsorNhowOLyl3lCqldVKwYOPVbnINAxMAu1y4x99iOSRZ2Bdu6ZJw6OgoLzNlOug+RrlYmICCRfA8fWiJgwstKrtu5+bg+H14ul/DNlfLAkqqVGm6G+T8HU+HOuuncQ9+2SDY/QMHYY/uXW1y8tS5wmPP0yrC87F9usvte7TsmUzSddcERgW/ufLKLn8ygbH2RTK2lHZv/4KI2t/vfbhGToMX7vUqodWE/j986V1DNzMb2ni4vD27gNA4q03h2YeUkEBrcZfhHXbVnxdupL7zkeQkFDjJkW33I4/IRH7yhU4Z02v33EP0lKTWZF1h0tERFqUmoacX3JsR87u1a5pAxIREZGgtdS2B9K82DYEKoh9Peuf2GipN5SC4R49Bm+fvpVe93dII++NKeS9+Ar+5GTsK1fQ+szhxD77JHi9B1YsbZ/knP5RyOf3OBYvwnC58HXpWu0gbHfpnI2oHSBeQwIaAgkDy769FVtPBSMujoL7Hwn870vPYd38e4PCtK5dg1FUWB5ThRgNAwyDkvMuwIyJwfH9tySfeQoJN11XMVl48LmycAGtLvs/LLk5eAYNpuCRJyJyWHhVfD2OxNunH4bXi/PTufXbiWliJiZhUM3PEyh48NEGVShEK8ec2diWVx4cXu95SB4Pra6cgH3lCvxt2pD7wXTMdrXfxzDbtqX4hpsAiH/4fnC763bcQ8MYOgxfu3YtLpmlxIaIiESEQz9mTlm2k+s/+pXduZqzISIiEon0lLpEPa8X66aNgf9tQCuqspZLZjU3TpvrDaWgFBVh+z3wM8577uXy4blZy1fjHn0+rov+RPbXP+A66xwMt5uEh+4jedQIrBvW45gzm5RBfUgeOyo0T1UfwvlZ4Kax65xzq73p7TnlVADs334DHk9IjtuUak1AQ70T0O5zR+M+7YzA39udt9V7XoGRtZ9Wl4/HcLvx9O1XaQaGv0Maea9PIf/1t8n6bjkl4y7CME1i35tKypCBxD3zBI7pH1c8Vy65ENu6tfiSWpH35lRwOusVW7i4zg9UbdT3Sf64557Ctmkj/phY/O1SKywr+3m2yDaR5fOQKguq0vTQRKvXS+Lfb8Cx6AvMuDhy3/kIX/ceQYdTdM31+NqlYt22ldi3Xq/zt1OBxwPOmBaXzFJiQ0REwqqqIefxDitOq8GyHbn831vLmf5rOmZzGNgnIiLSjOgpdYl21u1bMVwuzNhY/J0Pb8CODrRcqjK5YZrN8oZSMBzffh2oiuh8OK6L/698eO7BPwt/+w7kTfmAvOdfxp/UCvuKn2l96gkkXfHnyu2T6vtU9aF8PhyffwZUPV+jjLdvf/ytW2MpyMe24ueGHTNUgqxisWzeROyLzwW1y3oloA2DgkceDwwS/3IhjvpUF/h8JF1zBdbtpS18pn1C1s+/kTNj7iFJsMBNeH+nzuS//DrZcz/HM+g4jKJC4h++n6SJV1Q6V0zAkpeLbVnlp/MjnWvMBQDYlyzG2F+3dlS2n5cR93igmqbgyWfJWrmu2p9nS9OQStOqEq1tjjqcmA/fw7RayXv9bbzHHle3gOLjKfrHHQDEPfUYRn5eHb+jAxLuuQPrju34ExIqfe5qzsksJTZERCSsyoacTx4/kHED0nhr/EAWXHsC7112HMd0TKLI4+ORzzfyt+mrycx3hTtcERERKaWn1CXaWUvbUHl7HNXgwa3u0WPIe30K/g4dKi90OKtsx9QSOL74HAD36WfW3ArIMHBdfAnZS37AdfoIDK8Xg8pV3aGa32P/6QcsWVn4k5PL52hUyWLBffKpge8l1HM26tFmq9YqFp8Px2fzaHXxWNoMHYhz4fygQqlvAtrXvQdF1/0NgIR//ROKiuq0ffzD9+NYvCjwtPvkdzFbp4DViufEk6tMgpXxDh5CztyF5L34CqbFUvW5AmAYUTnryde9B55+AzB8PpzzPgl+w4ICEq/9K4bPR8kF43BdeHFQP8+WItgEXsI/JxH3xH8CCQ6Xq/o5NQUFABRffiXuM86qV0wl4y/F2+NILPv3E/vCM/Xah3PWdGInByo+8l57i6xf1raYZJYSGyIiEnZVDTnv3DqWl/84gL+f2h2nzcLSrdn86a1lzPkto8HVG2sy8rn2w5WsycgPRfgiIiItUw1PqTfntgfSfBwYHH5USPbnHj2GrOUHPW0+7RPcJ5yI4XaRdOPEqLu52mCmeSCxccaZQW3i75BG8Q0317hOKOb3OD6bF4hrxNlgs9W4rmf4qUBo52zUp81WtTdXS6tYEq79KymD+9Pq0j8FWuMYBq4zzsSf0qZRE9BFN03C17ET1h3biXvuqaC3c8yeQdzzTwOQ//QL+Oqa/LNY8Kd1xPD7q10lmmc9HWhHNSPobRLuvRPbls34Onai4LGno2auSFMJNoFnW7+O+MceJnnMObTt0YmkiVfUOKfG+enc+r+/22wU3vVvAOJefhFLRnqdNrds2UzC328EoOhvt+A5/cwWlcxSYkNERCKW1WJwyaBOTP3zsfRpn0iBy8d9n21g0szf2FcYGK5VnyTFvDWZLNuRy7w1mY0VuoiISItQ3VPqzbntgTQftg3rAfD1rP98jUoOvqF08inkv/A//AmJ2H9cSuyLz4buOFHAumUT1m1bMe123CcND3o7y57gPqPXe36PaeI4eL5GLdxliY1lP2IUNPzBqNoSFFUmN2oYAm6YJpgmsdM+xLpzB/6UFIpuuJmsH34h771p5D8ROO8aLQEdH39gkPiLz2LZsrnWTaxr15D0t+sAKLrub7jGXlivQzfnWU+uMYHEhv2bxRj79tW6vmPeHGKnTMY0DPKffxkzuXVjhxh1gqo0bZdK/n+epOSCcfgPa4fhcmG43Y0yp6aM+9zReAYPwSguLm8jFhSXi6SrLsdSkI/n+KEU/vPuescQrZTYEBGRiNe1TRyv/d8xXHdSV2wWgyWbs/jT5GUsWLcn6CRFel4JazPzWZeZz4J1ewFYsH4v6zLzWZuZT3qehpSLiIjUR9lT6vmPB5689ScmkrVslZIaEvHKKjYaMji8Nv7Oh1PwcKCyKf7Rh7Cu+rXRjhVpyqo1PEOHQUJC0Ns19vwe6+8bsW3ehGm34zntjNqP07UbvsO7Yni92L//tl7HLBdEgiLx9r9jX7wIx/xPcU7/iJgpk0m48x+1DgEHKPzbLez/ZR2F99yPv2s3oGkS0O7RY3CfchqGy0XCXTUPEjdysml12f9hFBXiPvlUCu/+d72P25xnPfm7dsNzzEAMvx/n3JpnylgyM0i85QYAiq/7G546JBJblGAqTf/zJCVXXEX+K5PZv3ojBf9+MKhdNyh5ZhgU3PMAADHvvI21NOlem/j7/4X911/wt25N3v/eqLX6rDlqed+xiIhEJZvF4C9DDufk7m24a+5aNu8v4q6567BbAx9AZq/OwOX14/Wb2CwGMXYrXl/ga6/fZM5vlRMf2UUeJkxdUf71T5P0AVBERKRerFZKLh5Pwj9vxZKfjyUzA3/HTuGOSqR6pomtdMaG76iejXoo18WX4PpsHs55n5B0/VVkL1gMMTGNesxIUGG+Rh2UPVVtSU8vn6lxMNMw8HdIq3f7pLIh156ThmMmJgW1jfuUU4mdMhn711/hPvOceh0XDgwvro4BGHv3knzR+fXav+/oPlWeW+7RY8gaOQr70u9w7N+Du027wM8vVC1qDIOCR56g9SlDcS5cgGP+p7irqobx+0m87iqsW7fg63w4ea+82aCbsY19roSba8w47L+swDl7BiWXXVH1SqZJ4t+uxZKVhadv/xb51H5dlCX6Eu6+rcLvor9DGgUPPlox0WcYeAcMDGq/DU2eeYcMxXXOKJyfzSX+wX+T9/Z7Na7vmPsJca++DBCoDGyhn7lUsSEiIlGlx2HxbN5/YCidxxf4AFvs8TNzVQZzfstk5qoM3v95Fx+vTC9/rSYGMOm0IxozbBERaQIbNmzgD3/4A4MHD+bRRx8NeibTtm3bOP7446td7vF4OO+88/jhhx9CFWrzFBNTfoPY1oKeSg+50oHC9o8/DHqgsNSdZddOjKJCTLsdX+mT7Y3GMMh/4ln8h7XDtm4t8Y880LjHiwTFxdi/+wYIfr5GuZqeqi79syHtk5zzA/M1XGfX3oaqTNmcDUcD52wE+1S3r0ManmMH4T75FFznnIv75FOC2q7Gm6ulbdI8F/6xUfru+3ocSfHEQNVAwt23Q3FxpXXiHnsY58IFmDEx5E1+B7NNm4YdtJnPenKNuQAA+7dLMPbsqXKdmNf/F5ipEhND/n9fA6ezCSOMTpXmIdUwYDuo9lUNnFNTpvDuf2NaLDg/m4vth6XVrmfZvo3Em68HAq3cGpJsjXYRn9h44oknmDhxYvnXNV2s1PdCRkREosv95/bEaqn6g4UBnNgthcuP78xfhx7OxBO7cP1JXbnplO78+biqn2IwgWe+2sQ/P1nD8h05+vdDRCQKud1uJk6cSJ8+fZg2bRqbNm1i+vTptW63Y8cOrr76anJzc6td57XXXmND6ZPdUjNv3/4A2FatDHMk0enggcJxf/1LUAOFpX7KB4d3PwLs9kY/ntm2LflPPw9A7MsvBJJWzZjjuyUYJSX4Onaq1wyT6tonARTc+2C92ycZe/ZgW/Zj4BhBzNcoj+ekUzANA9vaNRiZ9Z/TF+xT3fkvvUrOZ4vInfYJeW+/T+6HM5vs5mpDFP79H/jSOmLdvo24Z5/E/u0SnNM/wv7tEhxzZhP/VCAJkf/Es3j7DQjJMZvzrCf/4V3wHDso0I5qzqxKy63r1pJw378AKLj3gdDOC2rugh2w3YTJM99RPSkZfykACffdXXVLN7ebpKsvx5Kbg2fQYArvurfBx41mEZ3Y2LBhA++++y533nknUPPFSn0vZEREJPqM7J3K5EuOqXLZ238eyDPj+nL9yd245sSuXDm0C5cPOZw/H9eJs3sdBhzoQVv2Z+/UBHwmfLFhHxM//JX/e3s501fupthT8QnJ+gwqFxGRpvH1119TUFDAHXfcweGHH84tt9zCxx9/XOt211xzDRdddFG1y7du3cobb7xBx44dQxlus+XtV5rYWL0qzJFEn3oNFJZ6Kx8c3ojzNQ7lPmskxRMuxzBNEm+ciJFXfUI12tkPbkNVzc342hz6VLX7xJMxAOdXX9Q7LufC+Rimiaf/MfjTgn9fN9u0Kb8R71jyVb2PX++nv6OlMiEhgcL7HgIg7qnHSB47iqSJVwb+vHICAEVXX4vrj/8X0sPW5Qn8aOMaMw4A5+wZhyxwkXTtXzFcLtynj6DkiqvDEF3L0JTJs6J/3IEZG4t92Y845s2ptDz+wX9j/3k5/uTkQCu3JkjMR7KITWyYpsk999zDZZddxuGHHw7UfLFS3wsZERGJbocmKWrSOs5Bmzg7vVMTuGNED3qnJtAmzs7j5/fhvUsHMa5/B2JsFjbtK+KRhb9z7v+W8tSiTezIDpRRBzuoXEREmt66desYMGAAsbGxAPTs2ZNNmzbVut3//vc/zjmn+hL+e+65h6uuukqJjSCV3fizrVYrqjqpbaAwpa1d1JYqZMqGs3qPPKpJj1tw38P4unTFunMHCXfd3qTHbkrl8zXq2obqUAc9VZ3/9AuYdjuOr77Evqh+yQ3HZ4H5GnWp1igTknZUZQmKauZBQPUJimipTDCtVkwqX58ZpokJeAYPaZwDB/sEfpRxnReYt2L//tsKrcziH3kA22+r8LdpQ96z/613AlGC01TJM3/7DhRNDLSZin/wXuxff3Wg6unTucS9/AIA+c/+F3/nw0N67GgUscPDP/zwQ9atW8dFF13EokWLOOmkk2q8WKnvhYyIiESnsiRFaqKT8/u1Z9aqDDLzXbSOc1S7TWqik9lXDcFuNTAMg7H9O+DxmThsFlITndxx5pHccHI3Pvktg49/2c2OnBLe+3kX7/28i/5pieWzPRas38voPqmYQHKsnQ5JzX/4o4hIpCsoKKBTpwMtBw3DwGKxkJubS6tWrardrnPnzuzcubPKZdOmTaOgoIArrriCJUuad9uYUPH27QeAdcd2jOwszNYpYY4oOtQ6UNg0se7ehX3pd4EbdtJg5RUbTd26JSGBvBdeIfn8c4j54F1cZ58bMTekQ8WyeRO2LZsxbTY8Jw8P2X79XbtRfMVVxP3vJRLu+xfZw0+t283roiIcixcBdZuvUcY9/FTiXngG+9dfBRIT9a1EGTkKf7tUrHsqPixV5fDiQ7c9aAi4JTMDf2r70A4BbyifL5CErY5hkHDvXWSNPj9yYo5w/s6H4xk0GPvyn4h97im8gwZjycwk9qXnAMh/+kXM1NQwR9lClCbPGlvxDTcT++rL2Db9TvKFB94PypKfRddch3vkqEaPIxpEZGKjsLCQZ555hi5dupCRkcGsWbN4+eWXGThwYLUXK/W9kDmU3V7xjdVm0xutBEfnitSFzpeG69wmjs+uH1aepLj4uE7lSYqaOBwVf/aHjlZr47By+QlduHTo4Xy/OYvrPgw8cfrr7gPtp7KLPEyYuqL865V3nNawb6YGOlekLnS+SLCa47litVpxOComt51OJyUlJXW6HiiTlZXFU089xWuvvYbNVv/LphZ3fXFYG/xdumLZtpWYdb/hO+XUcEcUPj4f1u++Lb/56Bt2YtU38nw+nN99HdQuHfv3YDia+TnUFEyzvGLD6HN0pc+Hje7kE3HdfAsxTz1B4q03UXDiCZhBzl6oTiS9tzi+/hIA39Bh2Nu0Dum+vbf/E/O9d7CtWU389A/wjJ8Q9La2hV9jFBfj79QZ67HHYK1rYuLkkzCdTqy7dxGzfTP+elb72KfPwLonE3/rFIpeeR1Lbk6F94jqH9MqY4XTTi2viqh9/Yoa81yxLvk2qCRt7LKl+EKY9Gru/D17wvKfiHv1ZXj15fLXPaedDmPOq/M5UBeR9N7SUti+/RqjoKDS62VVT+aQoU3/71YQwnGuRGRi4/PPP6e4uJi33nqL5ORkrrnmGs477zymTZvGuHHjKqxbdrESqgsZj8dXqSLQ7Va5rwRH54rUhc6X0PD4K75ph/LnOrhzMvef25P7Pl2Pr5p54h2SnDy2YCODD0/mmE5JxDsq/9O6JiOf57/ezI3Du3N0+8Q6x6FzRepC54sEKxTnSiR1PWjVqhUbN26s8FphYSH2evYefuihh7jwwgvp3bt3g+JqidcXnr79cW7bCit+wX1Cy6wucMyZTcLdt1W4wedLS6PgwccCT2N7PIGhunNm45z3CZZ9e4Par7tNOzzRfv74fGF/2tzIzMSSk41psVByeHcIw8/Ufcs/sS5YgH31rzhvuI68qR82+E21Tu8tjfj3ELNgPgAlp40I/ftdQjKWm28l4f5/4XzwfgpHXQBxcUFt6pgb6FfvOnskbo+/7se2OvAcPxTHksWw8AvcXY6o+z5Mk/gnnwCg+KqJFJ9yxoFlPpqs3Vxj/Tvk3FV9UuNg/l27m/2/haHimDMbx3vvVHrdBGxfLYLpMxq96kt/V03I5yPhn7fWsIJBzN13kDXyvIisemrq64uInLGRkZFB//79SU5OBsBms9GzZ09cLhdZWVkV1i27WGnVqlW1y0REROprZO9UJo8fWO3y9DwX7yzfyc0zVnPGC99xxbsreOmbLfywLZuS0uHjms0hItL4+vXrx8qVK8u/3rlzJ263u17VGgBz5sxhypQpHHfccRx33HEsX76ciRMn8sorr4Qq5GarfID4qpW1rNk81TgE/Io/02rsKNr07UHyHy8g9u03sOzbi79VK8zYOKp5jqL6gcJRxjFnNimD+lQYKJwyqE+TD0a3bSxtQ9WlK8SEqaWow0H+i69gOhw4P59PzFtvBJJdpb3Ug77B7fNh/3YJ9o8/DHq7Rv17KCnB8W2gdV+D52tUo/iv1+DrfDjW9N3EvfJScBv5fDjnfwqA65z6t3BxN3DOhuOLBYG5CPEJFF/Z/IY9+4OsPAp2vRbvoPlLhyq796v5S81LWWvK6u7tGxxoTSkRmtho3749Lperwmu7d+/m9ttvr/ZiJdQXMiIiIoc6dFD583/ox4Pn9uKCfu3plByDz4RV6fm8+cMObvh4Fae98C0TpvzMrFWBIW8L1u1lXWY+azPzSc8rCcv3ICLSXA0ePJj8/HxmzpwJwCuvvMKwYcOwWq0UFBTg8XjqtL8vvviC2bNnM3PmTGbOnEnfvn158MEH+dOf/tQI0Tcv5YmNljhAvJYh4Abg+HYJluxs/G3bUjzhL+R8MIP9azaT9+IrYBjlPbQrMM1qBwpHixoTPldOaNLkhnX9OgB8R/VssmNWxdf7aArv+jcACbffUudEw8EJiri//iWo7Rr778H+3TcYxcX4OqTh6310g/ZVrZgYCu/4FwCxzz2Nsbf2iifbz8sCScTEJDwnnFjvQ5cNELd/uwS83jpvH/fsUwCUXPqXZjmDyDN0GL60tKrfx2g+SdqmUutNblM3uZubgwfEh2K95i4iExunnnoqmzZt4r333iMjI4O3336btWvXctJJJ1V7sVLThYyIiEhDlA0q752awB0jetA7NYE2cXa6tYnj7N7tuOuso5hx5fHMvup47jn7KM49uh0AXj+s21NAiTdQ6p5dHJjNcenUFYx59cdwfksiIs2OzWbjgQce4N5772XYsGHMnz+fSZMmATBmzBgWL15cp/116tSpwn9Op5O2bduSlJTUGOE3K95+AwCwbtwARUVhjqZp1XYTqkzBA/9h/6qNFDz5LJ7TzgC7HffoMeS9PgV/hw6VN7Bam37IdSjVkvCBpn3quLxi46jw/0x9nToFZiUc8kR2bYmGeiUomuDvwfHl50BptUYj9it0jbsIT/9jsBTkE//kf2pd3/nZvEBcI84ER/0nEnj7H4O/VTKWvFxsK1fUvsFBbEu/x/7D95gOB8XX3lDvGCKa1UrBg48BVEpulH0d7UnapqSb3C2Pqp7qxjDNKuqZIsAvv/zCf/7zH9auXUvbtm254447GDFiBAsXLmTSpEnEx8fj8/mYOnUqRx55JECNy4K1b19+hQovh8OqXnISFJ0rUhc6X6KP2+svH1Rummatg8o/XZPJfZ9VP5vDbjUYfkQbTunRhpO6tSExpurZHC98s4UbTupWr9kc0vLovUWCFapzxTCgbdvIen/KzMxk1apVHHvssaSkhP9p2BZ5fWGatOnTA8u+vWR/+gXeQYPDHVGTcU7/iKSJV9a6Xt7Lr+Mad1HVC0tnHzj278Hdph2xLz2H8/P5uE85jdwPZ0bWcJsg2b9dQvLY2tv/5MyYi+fExp/L0mrcaBzffE3e8y/juviSRj9etXw+Ugb1wVJNMswEzIQESib8BSwHfe70+4l5+02MwoJqtyM2FvcZZ4HXg+F2g8eDsW8f9rW/1RpWQ/4eWp9wLLZNv5P7+hTc551fr30Ey/7N1ySPG41ps5G95Ad8R1R//6f1SYOxbVhf8+9ekJL+8mecc2dTeMe/KPr7P4Lf7pILcS5cQPGEyyl48rkGxdAQTfHvUNVzhjpS8OCjjT4PojmJhPfOFvG5JZKU/buQnl4p4Q2BBKG/QxpZy1dHXIIwHNcXETk8HOCYY47h/fffr/T6iBEjWLBgQZUXKzUtExERaYiDkxiGYeCw1XxTYeTRqXRrE8eEqZWf5GodayO72MsXG/bxxYZ9WC0Ggzsnc0qPQKLjsAQnEJjN8dO2HOalZCqxISISpNTUVFJTU8MdRstmGHj79cex6Atsq35tUYmNkDxpabXiOfFkDIcVj9uHL60jjsWLAv/Nm4N71HkhirbpRNpTx9YNZRUb4W1FVVbhUx0DMAoKiPvv83XarwFQXIxzzqx6xVXfvwfL1i3YNv2OabPhOeXUeu2jLjwnDcd15tk4P59P/AP/Jm9y5QHLANbNv2PbsB7TZgvJ3A/38FNxzp2N/euvIMjEhnX1KpwLF2BaLBRdf1ODY4h07tFjyBo5qtEG1LcUZa29arvJrdZezUhp1VPSlRMwDaPC37uqniqL2MRGTWq6WNGFjIiIRBqDwJNzZX8+O64fJvDV7/v46vf9bNlfxNJt2Szdls2jX/xOj7ZxDOqczGdr9wCwYP1eRvdJxQSSY+10SArTkEsREZEgefseSGy0JOU3oap7Ar8eN6H83bpTdP3fiH/6CRLuuYOs086AuLjQBd0EIqm1hpGdhXVPJgC+I49q9OPVJNgEguvMs/EdeSAJY924Hufn82vdrvji8XiPH4Jpt4PDgWXTJhIef7jW7er79+D4ciEAnsFDMJOaZt5p4T0P4Pjic5zzPsG29Hu8Q0+oHNdngaHhnhNOwmyV3OBjliVt7D/9AIWFEB9f6zZxzwdma7jOH4u/+xENjiEqlCZppQF0k7tFKmtNeWjVk79DmqqeDhGViQ0REZFoUDabIzXRyfn92jNrVQaZ+S5S4h2kJjo5un0i153UjW1ZRXz1+34W/76PVen5/L6viN/3HehJnl3kqVD58dOk4TUed01GPs9/vZkbh3dXpYeIiITFgQHiK8McySFK2zw12hPEVitFf/8HCf/4e6VFDbkJVfS3ScR8+D7WHduJe/5pim6/KyThNpVaEz6A2aZNkzx1bN2wAQBfx06YCeH9nBRsAqH4ur9VuEFs/3ZJUIkN158uqXhj2ecj9p3Jjfb0d4X5Gk3E17MXJeMvJXbKZBLuu5uceQsrtWtzzA/M13CNPDc0x+x2BL5OnbHu3IH9h+/xnD6ixvUtmzfhnDUDgKIbbwlJDNJy6CZ3y6Sqp+BE5PBwERGR5iA10cnsq4YwefxAxg1IY/L4gcy+agipic4K63VJieOy4zvzxiUD+cfpR2CppXX2H99cxj3z1vHu8p2s2JlLodtbYfm8NZks25HLvDWZof6WREREglKe2Fi7BrzeWtZuGo45s0kZ1IfksaNImnglyWNHkTKoT7XDmevFNHF8Pj9QpXnIgGJ/hzTyXp9Sv5tQ8fEU3B940j7uhWewbN0SgmCbkNUaSOhUoayq1cjOxvnhe40eyoHB4eFtQwUHEj6HDlkuYxoGvrSOlRIN9d2uxsHOpX/W++nvkhIc33wNgPv0pktsABTddidmXDz25T/h+GRmhWXG/v3Yf/g+ENfZoUlsYBi4h58KgOPrr2pdPe7F5zD8flwjzsLXt19oYpAWxT16DFnLfyNnxlzyXn6dnBlzyVq+WkmN5q606sk17qJAklpJjUpUsSEiItKI6jqb448DO9I/LanK2RzJMTZySrxsySpiS1YRn5a2qjKAtCQnnVNi6Z4SX57QUAsrEREJF1+3I/DHJ2ApLMD6+0Z8vXqHNR7HnNkkXTkBDnlK3ZKeTtKVE+qfcDj0OHM/wbngM0y7nezPv8aStT9kT1q6R5+P++RTcSz5ioR77iDv7cozKeulsatYytjsFdpzlvGndcTXpSuO778l6abrKNy1k6JJtzfakHTr+nUAeCMgsVHvNjMNaE9T3dPfBlB43d/q/XtgX/odRlERvtT2+Pr0rdc+6suf2p6i624k/on/kPDgv8k6ZxSUJhYdC+dj+P14+/TD3/nwkB3TM/xUYt+dEpizUQNLRjoxHwRmfxT9bVLIji8tkFp7iVSiig0REZEIZRzy5/MX9uPTa4bw9Ng+XD2sC8OPaEO7BAcmsCvPxdKtObz78y7yXT7gQAurS6euYMyrP9Z6vDUZ+Vz74UrWZOTXKc76biciIs2YxVJ+c9O2KsztqHw+Eu6+DUyzUhukshvCCXffDj5fgw5jFOSTcNdtABTdcBO+3keH9klLw6Dg4ccwbTacn83D8cWChu2PJqpiAfB6iX/gHgCKb/x7paeOc2fOo+imwE3f+MceJuHWmxqt0udAxUavRtl/XZUlGvwdOlR4vbYKn/puV7btwU9/F190MQDOz+aCx1Ov78PxRWkbqtNHNFpSqiZF1/0NX7tUrFu3EDv5tfLXnZ+VtqE6e2RIj+c+6RQA7Kt/xdi7t9r1Yl9+EcPtxjPkhCrnf4iISP2pYkNERCTCHDybY9zANKav2E1mvovWcQ7aJjg5KcHJSd3blK+/v9DN1GU7eWf5zkMfRC1nAJdO/ZlBnZM5rnMyAzomkeCs+DHg4BZWdZnNUd/tRESkefP264/9x6XYVv2K66I/hS0O+9LvKjyZfijDNLHu3oV96XcNeho27j8PYk3fja9rN4pu/ke991MTX89eFF91LXH/fZ74u24P3Fx1OmvfsApNVcUCEPPuFGwbN+BPSaHopluqHCxdeNe9+DqkkXDnP4idMhlLRjp5r0wOajBzXVg3BBIb3ghJbED9e6kfvJ1j/x7cbdoFX3Fz0NPf7jPPxvnlQmybfifmnbcpufzKOn8PjkWBweFNOV+jgoQEim67k8RbbyLuyUcpufgSTGcMjkVfBOIaOSqkhzPbtcN7dF9sa1bj+GYxrrEXVlrHyMkm5q03ACi6SbM1RERCTYkNERGRCFM2m8NuNXA6bYw5OhWPz6zQ1upgbeId3HRKd87udViVLaxSExxkFrhZm1nA2swCpi7bicWAXqmJ9Dwsnu5t4jmqXTwL1gWeNgumhVV6Xgk5xR4MqNN2B9OQcxGR5s3bbwAAttW/hjUOS2ZGSNerim3lCmJf+x8A+Y89DbGx9d5XbYpuvR3ntA+xbd5E7P9epPhv9bhhWksVi2kYJNx9O1kjRzW80qSggLjHAvNBiibdXmVSo0zJFVfhb9+BpIlX4Px8PsnjRpE79SPMww5rWAwHxWLduQMA31FHhWafoVLfNjOl2xkOKx53/aqOzMQkCifdTuKdtxH/+COUXHgxJCQEvb1lx3ZsG9ZjWq14TjmtXjGEQsklE4h95SVsG9YT9/QT+FNSMIoK8bVOwdsn9LMt3MNPxbZmNfavv6oysRH7+itYCgvw9umH+4yzQn58EZGWTokNERGRCFTX2RwHO7h/tQk8cUEfWsc5+HlnDst35PLzjhx25JSwJiO/yvZRZS2sylx0TNqBtlil//PBispPvh663U+ThtcYpyo9RESat/IB4qt+DVQFhKE9DW43jjmzglrVn9q+fsfw+Ui49WYMv5+ScRfiOfX0+u0nSGZiEoX3PkDS9VcT/9RjuC68GH9axzrto6mqWADiXn4B655MfF26UnxZ7ZUA7nNHk/PxJ7Sa8EfsK36m9agR5Lw/HX+Xrg2eBWL7fQMA/sPaYbZOqdf301yVXHoFca/8F+vWLcS9/AJFt/4z6G3L2lB5jzses1VyI0UYBJuNwnvup9WfLyb2pefKP79as7NIOa4vBQ8+FtJhy55TToWXX8CxeFHl97jCQmJf/S8ARX/7e3je/0REmjklNkRERJqJg1tYnd+vPbNWZZS3sEpNdDKydyoje6cCkJnv4uedOcz8NYOfd+bWuN+Pfqn+xkdNTn/hO9onOUlNdNI+0Un7pBicNguxdgtt4h31rvQQEZHo4O3ZG9Nux5Kbg2XHdvyHd2nS41vSd5P018uw//QDUHlodRmTwFwCz9Bh9TpO7BuvYF+5An9SKwrue6Te8daF68KL8bz1BvYflxL/77vIf2VynbZviioWAGPPHuJeeBaAwrv/XT7QuTbe44eQM3chrf70B6xbt9D6zOHgjMGy78AsA19aWp1vVEfU4PBI43BQeOc9JF39F2JffI7iy64MulLG8WXpfI1wtaE6mNtd5e96Y7RYcw8Zhmm3Y925A8uWzfi7H1G+LPadt7BkZeHr2g3XeReE5HgiIlKREhsiIiLNxMEtrAzDYGz/DtW2sDo40bEuM7/KFlYX9GtPm/jADQgTyntwm6X/u7/QzSe/ZVbaLs5upcjjI9/lJX+vl417C2uMu66VHiIiEiUcDrw9e2Nf/Su2Vb/ibsLEhn3JYpKu+QuWffvwJ7Wi+LIriXvh6cANz4NmSpTdAPUfdlhgeHgdKwAs6buJe+RBAAr/dR9mamrovomaGAb5jzxB6zOHEzNzOiWXXoHnpOD//bTsqfzvd1XqXcVSKv6JRzCKCvEMPBbXmLF12tbX40iy535O8ugzsW3biplfscq0PjeqbRsDFRs+JTaq5BozFs9Lz2H/ZQXxT/6Hgv88GcRGLhxfLwYiILHh85Fw9+1VLgp5izWAhAQ8xx2P4/tvcXz9FSVliQ23m9iXngeg6IabwaZbbyIijUHvriIiIs1IKFtY/WFAB3qlVt8ial1mPp/8lllpu/9d3J9OybFk5rvIyHeRme8iM6+EjHwXazLy2ZpVXOX+LAb8+xzdaBARaU68/fqXJjZW4h51XuMf0O8n9oVniH/4fgy/H2+ffuS+MQV/t+54Bx5Lwt23VWjBZLY9DPJysf+6kqSJV5L3ypt1ugmZcNftWAry8Rx3PCUTLm+Eb6h6vn79KbnsCmLffI2EO/9B9hffgN1e4zZG1n4S/n03Me+/A9RSxZLWsd5VLADW3zcSM2UyAIX3PlivVjxm27YYLleVcdbnRrV1Q1nFRuQMDo8oFguF9zxA8rjRxLz9JsVXX4uve48aN7H/8H1gjkW7VLx9+zdRoNXE0oQt1sp4hp96ILFROnQ95uMPsO7ehS+1PSUXXxKS44iISGVVTyEVERGRFqOshVXv1ATuGNGD3qkJtImz0zqu5nYRNW2X4LRxRNt4TuyWwrj+Hbj2pG7cN7IXH/1lMFP+PLDK/flNmP5rOst35DTCdykiIuFQPmcj1APEfT7s3y7BOf0j7N8uAZ8PIzeHpMsvIeHBfwfmXfxpPNnzFuLv1h0A9+gxZC3/jZwZc8l7+XVyZsxl/6oN5E75ANPhwDlnFok3XQd+f1AhOBZ8inPOLEyrlfzHnwFL019eF/7zbvwpKdjWrSX2jVeqX9E0cX78ASknHkfM++9gGgau084Aw8A8JOFQlkTw9uzVoKfa4x/8N4bPh+vskXiGnVSvfdiXfoc1I73K5AtUvFEdDOuG9YAqNmriOWk4rjPOxPB6iXv4gVrXL5uv4Sk9n8KpqVqsHcw9/FQA7N8sDlR9+XzEPv80AMUTbwCnM2THEhGRilSxISIi0sLVpYVVKLYrc2ilh80Cv+zKY+KHv3L84clMPLEr/dKSGvrtiYhIGHn7DgBKB4iHiGPO7EqVF77SWQDWvXsxnU4KHnmCkvGXVr7RarVWelLbc9oZ5L36FklX/JmYj97HjIun4LGnar5JW1hIwj9vBQI3L319+obmm6sjs3UKhXf9m8RJfyPu0YfxdeyM4XZVGK5t2bqFxNv+juOrLwHw9j6a/CeexTt4SJU/SzOlDWTtx7noC2LeeJWSK66qc1y2H5binPcJpsVC4d331fv7C+mN6pISrFu3AKrYqE3h3ffh+HIhMbNnUPzzjXiPPa7adR2LFgIR0IaK4FunNbTF2sG8AwfhT0jEkpODbdVKLDu2Y9v0O/7kZEou+0vIjiMiIpWpYkNERERw2CwYpTdwAi2sgvuIUJ/tqqv0eP1Px3DhgA7YLAY/bs/hivd+4e8zVrM+s6DC9msy8rn2w5Wsyciv5ggiIhIpfH36AGBN342xb1+D9+eYM5ukKydgOaTdjGXvXqx79+Jrexg5cxZQ8ufL6vT0uHvkKPJffAXTMIh963Xi772rfLZUVeKf+A/WnTvwdT6cwlv/We/vJxRKLpmAt2s3LAX5tLrizyRNvJLksaNIObYPCdddTcopQ3F89SWm00nhnfeQ/fnXeAcPAaqpYvnt98CgbyDhrtuwL1lct4BMk4T77g7ENv5SfD3rn0QI5Y1q66bfMfx+/K2SMdu1q3dMLYGvT19cf/w/AOLvv6fa3wXLrp3Y1q3FtFhwn3JaU4ZYJc/QYfjS0ipVIZUxDQNfA1usVWKz4TkxUJEU+9JzxD8USOQVX3E1ZkL1LV1FRKThVLEhIiIiTaqmSo+jOyQxYXBnXl+6jbm/ZfLN5iy+2ZzFGUe15ephXejeJp55azJZtiOXeWsyObq9LhhFRCKZmZiEt1t3bFs2Y1u1MtCupr58PhLuvg1Ms/K8BQLVf9hs9e7z7xp3EUZJCYk3X0/cyy9gxsdTdPtdldaz/raa2JdfAKDgkcchPr5exwsVx6dzyysRDmZJ303sx+8D4D75FAoef7rqeQlVVLEU3/h3bGvXEDPtQ5KunED2Z4vwlw1Gri2euZ9gX/YjZlwcRf+4o+7f0EHKblRb0tMrDH0/mBkTg2fgoFr3Zdt4UBuqMLdMigaFt9+Fc+Y0HN99g2PhfNxnnlNpnbI2VN5BgzFbpzR1iJVZrRQ8+BhJV07ANIwK50xZsqPgwUdDMzi8lGPObOzfB1qhxcycXn4sX5duITuGiIhUTRUbIiIi0uRqqvRIaxXDv87uyQeXH8fZvQ7DAL7YsI+LJy/npmmr+GztHgAWrN/Lusx81mbmk55XEo5vQ0REguDtF5p2VGWDgaudtwBYM9KDnrdQlZJLJpD/8GMAxD/5KLHPPxNYUDbTY9qHJF3zl8DsiNHn4z5rZL2PFRJlyZ4qlCV7/Mmtyf1gRq1DoCtubJD/9At4jh2EJSeHVhMuxsjLrX07j4f4B+8FoGjiDfjbdwj+mFUpvVENVDkLxASMkhJaTfgTFBRU3v7gXa0vHRzegAqSlsTfqTPFf50IBOal4PNVWqcssREJbajKuEePIe/1Kfg7VDz3/B3SyHt9Cu7RY0J2rLIKskq/G6ZJ4s3X4ZgzO2THEhGRypTYEBERkYjUJSWOB0f15t3LDjyF+d3WbHJLvABkF3mYMHUFl05dwZhXf2y0ONT6SkSkYcoHiP/WsMRGUw0GLvnrRArKWjE9cA8Jf7+RlEF9SB47iqRr/4ptw3pMw8DdkOqTEAkm2WPJycb+w/d133lMDHlvvYevQxq2jRtIuvovVd7crrDJlMnYNm/C37YtxTfcVPdjVqHaG9VpHSm69Z+YcfE4lnxF8kVjMLKzqt2PdeMGAHxHanB4sIpuugV/cjK2tWtwfvhexYVuN/avvwr8bwQlNqDqFmtZy1eHNKlRWwUZQMLdt9f6OyMiIvWnxIaIiIhEtB5t47n/3J5YqrlrYwBXDOmMv4Ze6A1xcOsrERGpu/LERgMrNppyMHDx326h8O+B4eCx77xVaaYHpknCrTeF/Ynsxk72+FPbk/f2e5ixsTi+XEj8ff+qdl2jIJ/4Jx4BoPDWO0I6X6C6G9VFt91JzvRP8LdujX35MpLPH4klI73Kfdg2lFVsKLERLDO5NUU3BX4P4h99CIqLy5fZf1yKpbAAf9vDyquyIkppizXXuIsCrdZC2H4KgkgqmibW3bsaVEEmIiI1U2JDREREIt7I3qm8NX5glctM4I0fdjD6lR94+qtN/Jaeh1lFkqMulRfpeSWszcxnXWY+C9btBdT6SkSkvrx9Azc9rZt+r7VdUE3KBwNXszzUg4GL/nEn/tL5GZH6RHZTJHu8AwaS9/zLAMS9/AIx706pcr3YF57Fsm8f3u5HUDLh8nofr1rV3Kj2HnscObM+w9e+A7Z1a0kefRaWLZsP+Sa8gfMPVWzUVfGVV+Pr1Bnr7l3Evvpy+evlbahOOwMsLe/WUlNVkImISPVa3r8+IiIiEtWMQ/4cfkQKCU4rewvcvLt8F5e/+wsXvP4TLy7Zwsa9BeVJjpoqL0zTJCOvhB+2ZfPhil2MefVHLp26gglTV5Bd7AGarvWViEhzY7Zrhy+1PYZpYlvzW/13dNC8hUrHaITBwPYfvsdSWFjt8kh4Irs82VPNMOxQJXvcY8ZSeOs/AUj4x83YllZsbWXJSCeudKB64d33gd3eoOPVla9Xb3LmLMDXtRvW7dtIPu9srL+tLl9u3boFw+PBjIvH37FTk8YW9WJiKLz9LgDinnsKI2s/AI4vFwKR14aqqTRlBZmIiFTNFu4ARERERILROs5Bmzg7qYlOzu/XnlmrMsjMd3HbGUfSOtbO91uz+Xz9Hhb/vp/duSVM/nEHk3/cQVpSDEO7JvPFhn0AfLp2D23iHaTnlrCv0M2eAjfbsooo8fqDisNqwL0j9bSniEhdePv1x5qZgW3VSrzHD6n3fjwnDwebDbzeCq/7O6RR8OCjIe2hHxVPZJcme5KunIBpGBgHVSyGOtlTdOs/sa1fh/OTmbS6YjzZcxdi3b0LS2YGzmkfYhQV4TnueNyjzmvwserDf3gXsj9ZQPLFY7GtWU3yBeeS+85HeAcdh2PWdAB8HTpAI7WubM5cF16M978vYFuzmrinHsczeAi2tb8FZs2cfEq4wwuLsqSiJT29wu9dGdMw8HdIC1kFmYiIVGaYVfVqaMH27cuv8DnH4bDidmvYk9RO54rUhc4XCZbOlYrcXj92q4FhGJimicdn4rBVLEAt9vj4ZnMWC9bt4avf9we9b6vFoFOrGLqmxNElJRaHzcJr32+vtF6MzcJ1J3fjomPSsFU3+CNMdL5IsEJ1rhgGtG0buj76zZGuLwLiHrmf+KefoHj8pRQ8/UK99xPz1hsk/uNmvL16U/Dw41j2ZOJPbR+4eRjqHvrfLiF57Kha18uZMTfQGinE6nKuOObMJuHu27AeNAvEl9Yx5MkeCgtJHnMO9lUrMW02jEMSTAV33ENx6WyScDFysmk1/o/Yf/oB0+HATEjEknXg84AvLY2CBx8L7c8lAjT2e4v9y89J/tMfMKnYmq25/jyD4Zgzm6QrJwBUmVTMe31KRP5cWuq/Q1I/Ol8kWOG4vlBi4xC68JD60rkidaHzRYKlc6VhZvyazn8WbsRfxacdAziz52Gc1asdXVJi6dQqBpv1QJJkXWY+E6auwIDyi/iDd9OzXQL/HNGDvh2SGvebqAOdLxIsJTaajq4vAhyfzKLVlRPw9D+GnIVf13s/ySPPwL78Jwr+/RDF190Ywgir4PORMqhPrU9kZy1fHfKkCtTjXPH5sC/9DktmRqMlewBi3n6ThFtvqjR3xAQwjMi4mVtYSPJ5Z2Nf/WulG/GRftO5vhr7vcUxZxZJV0yo/PfeTH+ewWqypGIItdR/h6R+dL5IsJTYiAC68JD60rkidaHzRYKlc6XhyhIUh5ry54H0Sq3+A1NmvovLpv5cqfXVn47tyNs/7STf5cUAxg3owHUndSUpJjT9xNdk5PP815u5cXh3jm5ftxvGOl8kWEpsNB1dXwRYtm6hzfEDMB0O9m1Jr9cMBuvGDaSceBym1cr+X9ZhpqY2QqQVhfOJ7Ig8V8qSPbt3V7rBDY2f7Amaz0fKsX2wpEd4nCHUqOdLtPy9h0sTJRVDJSLfWyRi6XyRYIXj+kIzNkRERKRFqK7yojqpiU5mXzWkvPXV2P4dyltfjenXnucWb2bumj1MW5nOoo37uOmU7ozs3Q6jmgGuwTp4yHldExsiIpHK36Ur/sQkLPl5WNevw9e3X533EfPBu0BgWHFTJDUA3KPHkPf6lEpPZDfGTI9oYF/6XYWfw6EOHqjeGO25gmVf+h3W9MiPM1pEy9972FitLfP7FhEJM0vtq4iIiIhEr7Kh471TE7hjRA96pybQJs5O6zhHrds6bJbyRIVhGOXzPFLiHPx7ZC9e/mN/uqbEklXk4d5P13PdR7+ydX8REKi8uPbDlazJyK/1OOl5JazNzGddZj4L1u0FYMH6vazLzGdtZj7peTP3vFgAACOSSURBVCW17mNNRj5/fXdFUMcTEWlyhoG3NJlhW/1r3bf3+XB++B4AJRePD2VktXKPHkPW8t/ImTGXvJdfJ2fGXLKWr25xSQ2IkoHqdTh+uOOMFvp5iohIJFLFhoiIiDRrNVVeNNSgzsm8e+kgpi7byetLt7NsRy7/9/ZyLh3cidwSb6XKC5fXz94CF3sKXOzJd7O3wEVmvosPVlR+CjK7yFOhhdZzf+hLWlIMHZJiqox93ppMftqWw7wUVXqISGTy9uuP4/tvsa1aietPdUtO2BcvwpqRjr91a9xnndNIEdZAT2QD4E9tH9L1Gku0xBkt9PMUEZFIpMSGiIiINHsHJwIClRcNaxd1MLvVwl+GHM5ZvQ7jgc82sHxnLm/8sANL6SGmr0zn+y1ZZBV7KXB5632cv01bDQRaabVLdJLWKobWsXaSY220S3Qyb80eIFDpMbpPKiaQHGunQ1JMA79DEZHQ8PbtD4BtVd0rNmI+DLShco29EJzOkMYlwfMMHYYvLa3WgeqeocPCEN0B0RJntNDPU0REIpESGyIiIiIh0LFVLMt35pZ/7S+97vf4TbbnHGgl5bRZaJfgoF2ik8MSnLRLcJKa6MDl9fPc11sq7ffsXodR7PGzK7eY3bklFHv8ZOYHKj2qcmilx0+ThtcYd0OGlYuI1IW33wAAbKtXgd8PluAq54zcHJzz5gBQUsdKDwkxq5WCBx8j6coJmIZR5UD1ggcfDf/g5GiJM1ro5ykiIhFIiQ0RERGRELn/3J7c99kGfP7KTzNaDbjtjB6M7d+hygHj6zIDszEOHXL+5+M60Ss1kHAwTZPsYg+7ckrYnVvCoo37+HLjvmqHoVsM+Ot7vzDo8GSO69yKfh2SiLFXvOmgYeUi0lR8R/XEdDqxFORj2bYVf7fuQW3nnDUDo6QEb6/eeAcMbOQopTbRMlA9WuKMFvp5iohIpFFiQ0RERCRERvZOpVtKXIWKiTKTxw8sT1BUpWzIeWqik/P7tWfWqgwy810VhpwbhkFKnIOUOAf90pI4u3c71mXmV3m8lDg7WUUeVu7OY+XuPN5YCnarQd8OSfRsF0+3lDh6HJZQYVi5WliJSKOy2/H2Ohr7yhXYVv+KO8jERsz77wClQ8OrSAxL03OPHkPWyFHYl36HJTMDf2r7QBuiCHtiP1rijBb6eYqISCRRYkNERESkERxaeVGbhg45P/R4z4ztS2KMjWXbc1i+M5flO3LYW+Bmxc5cVhzUMqtMXVtYiYjUh7df/0BiY9WvuM+7oNb1rb9vxL7sR0yrFdeFf2z8ACV40TJQPVrijBb6eYqISIRQYkNEREQkhIKpvKhOfYacH3y8cQPTmL5iN5n5LlLiHaQmOumUHMsF/Ttgmibbs4tZvjOXOaszWJWeX+X+DGBc/w5kFblJqSFmzeYQkfo4MEB8ZVDrx3wQGBruPn0E/tT2jRaXiIiIiEQXwzTNYB4ibDH27cvn4J+Iw2HF7faFLyCJGjpXpC50vkiwdK5EJ7fXX155YZpmnSovGnI8p9OGy+UN6nhrM/K59J3KLawO1rNdAkO7tuaErq3pn5aE3Xpgn098+TsfrNjNxQPTuPX0HiH5PqTphOq9xTCgbVsltmqi64uKbD/9QOtRZ+Jrl0rW6o01r+zzkXJsH6zpu8l9/e2gKjyak5Z+rkjd6HyRYOlckbrQ+SLBCsf1hSo2REREREKsPpUXTX28sjb1h7awGt0nlY17C1m/p6D8v7d+3EGc3UrfDon0Sk2gf1qSZnOISL14j+6LaRhY92RiZGZipqZWu67966+wpu/Gn5yM+6yRTRiliIiIiEQ6JTZEREREWqDqWmZNPLErqYlO9he6+WFbNt9vzeaHrdlkF3v4cXsOP27PqbAfzeYQkTqJj8fX40hsGzdgX70Sd+pZ1a4a80FgaLhr3EXgdDZVhCIiIiISBZTYEBEREWmBahtW3ibewblHp3Lu0an4TZMNpZUbCzfsq3J/BnBe31T25Ltol1j9DUjN5hARb7/+2DZuCAwQP6PqxIaRl4tz3hwASv40vinDExEREZEo0HjNnkVEREQkojlsFozSnlSBFlZVfzS0GAa9UhN55LyjmfLngVWuYwKzV2cy6pUfGP/2cv77zRZW7c7D5684zm3emkyW7chl3prMkH4vIhI9vH0HAGBb9Wu16zhnzcAoKcHbqzfeAVW/74iIiIhIy6WKDRERERGps0Nnc4zr354Newv5LT2fDXsL2bC3kDd+2EFyrJ2BHZPo3T5RszlEBABv334A2FatrHadmPcDbahK/njJgaFAIiIiIiKllNgQERERkaBVN5vjiqFdSE10klXk5vst2XyzOYvvt2aRU+xh0e/7WfT7/gr7qetsjqZsYaV2WSKNy9svULFh3boFIy8XM6lVheXWTRux//QDpsWC66KLwxGiiIiIiEQ4JTZEREREJGi1zeZIiXMwqk8qo/qk4vX5Wbk7jyk/7eTbLVnV7jPObuXqD1bSLSWOrm3i6JYSS9eUOFITneWtsg5uYdXYyYamPJZIS2S2aYMvrSPW3buw/bYazwknVlju/OA9ANynj8Cf2j4cIYqIiIhIhFNiQ0RERETq5OBZHIHZHFW3ibFZLQzqnMygzsmsy8yvUKFxsCKPjxU7c1mxM7fC6zE2C+2TnHRsFcPPpcsWrGucFlbpeSXkFHswSo8Bapcl0pi8/foHEhurVlZMbPh8xHwYSGxoaLiIiIiIVEeJDRERERFpMofO5nj14gE47Ra27C9ia1ZR+Z87ckoo8frZmlXM1qzi8u2ziyu2sPr8uhNIjrVXeaxgW0qZpsmYV3+s9Pqh7bJ+vOXk8gqShhxPRMDbtz/O+Z9iW72qwuv2JYux7t6FPzkZ91kjwxSdiIiIiEQ6JTZEREREpNFVN5ujQ6sYUhOd9E6tmAjw+Py8t3wXL36zBb9Z/X7PfOl7urWJY2DHVhzTKYmBHVvRvrSyoqqWUn7TZHt2MeszC1i/p4B1ewrYsKcgqO/hrP8u5cjD4jnqsASOahf4s2tKLDarpdrjiUjVyuZs2Fb9WuH1sqHhrrEXQoyqpERERESkaoZpmjVcKrY8+/blc/BPxOGw4nb7wheQRA2dK1IXOl8kWDpXpC4i/Xxxe/3lszlM06wwm6M61bWwOrVHG7ZmFVWo5iiTEmenZ7t4Vu7Kp8jjI85h5cRurdm6v5gdOcWUeP2VtrFaDNKSnOzIKam0rGOrGDLySvBV8anZZoGOrWI5vHUsy3bkUuzx0TrWznN/6BvRLaxCda4YBrRtqyROTXR9UTXLju20GdQX02Zj35Z0cDox8nJp0/dIjJISsucvwjtwULjDDCudK1IXOl8kWDpXpC50vkiwwnF9oYoNEREREWkSwc7mqMqhLayuHHo4vVITyS5ys3JXHit25fLLrjzWZOSTVeTh+6055dsWuX18vn5f+ddOm4UjD4unZ7sEerZLoFdqAke0iWfz/kImTF1R6Vj/Oa83XVPi2Ly/iI17C9iwpzDw595CCt0+tmUXsy27+nZZP00aXseflEjz5+/UGX9yMpacHGzr1+LtfwzOWTMwSkrw9uyF95hjwx2iiIiIiESwiE9sXHnllYwaNYpx48axYcMG7rjjDrZv386FF17IbbfdVt7nuKZlIiIiIhKdqmth1TrOUb781CPbcuqRbQGYtSqdhz/fWGX7KosB153UlfHHdcZmqfw5saZjxditHN0+sUKLKdM0ee/nXTy7eHO17bL6tE/kp+3ZHNc5WZ9NRQ5mGHj7DcCxZDG2Vb/i7X8MMR+8C0DJxeMDj+uJiIiIiFSj5tr/MJs9ezbffPMNAG63m4kTJ9KnTx+mTZvGpk2bmD59eq3LRERERCR6pSY6+f/27j2qqjr///jrwAHkoijSV03Jr46ISV4wyFmSs+RSjcoPL4xpt3FmxPRXlo7XpFKRRsWJlc3PcfKSMt8vEjRmmJlfJ3QmHcVJqzEq0XL8moyBKKRc5L5/fzSd6XDzHAbhnHg+1nK5zmfvc/aHvV6Ab997789bs0cr9ZEQTR1xu1IfCdFbs0erV1ePJvefNKyPfv9ISJPbfv9IiGbec0eTTY3WHMtkMunhu/s1ezxJ+rSgVE/8IVc/2XFSaSfz9fWNmkb7fFZQqv/7+il9VlDa7OfANmfPnlVcXJzCwsKUnJwsW5+6e+HCBd1zzz2NxjMzM3XvvfcqODhYv/jFL3T58uW2nnKnVnvXcEmSOfeUXP/+hdzePy7DxUVV06Z38MwAAADg6By2sfH1118rOTlZAwYMkCQdPnxYZWVlWr58ue644w4tXLhQu3btuuk2AAAAODd3s4vlbodvHmFl2z9hTQ3+vpXHaup4SROGKG5EH3m5uerLkht6+b2/a+Lm43r+nTx9lH/N8p/u3110HK3X2oudLl68qMcff1zXrl2zGj958qRefvllrV+/XgcPHlRVVZWSk5Nv1fQ7pdph3zY2PpbHP+/WqI6MVn2v3h05LQAAADgBh21sJCcnKzo6WiNHjpQk5eXlacSIEfL09JQkBQUF6dy5czfdBgAAgM7l20dK3dnLR8ujB+nOXj7q6eVmeXxVex0vpJ+vnokO1DtzR2v5fYEK+g8fVdcZ+p/Tl/V45ilNfvWEXn7v7zqQVyRJ+uOZIuUVlup0Yam+ut54EXO0rLUXO82ZM0fTpk1rNH7+/HmtWrVKY8aMUe/evTV16lR98sknt2LqnVbtsBGSJPPHp+SZul2SVDVtRkdOCQAAAE7CIdfYOH78uHJycvT222/rhRdekCSVlZWpX79+ln1MJpNcXFx07dq1Frf5+vq2+/wBAADQcb59pJSbq0kmk0lThvdRTZ1h190XbXk8b3ezpg7voynDeuuzwjK9eeor7fmkQJeuVSrtZL7lc0oq7Ft0/LOCUv2/w3/XUz8aaLX2R2fV2oudNm/eLJPJpF//+tdW4w2bHefPn1f//v3bbsKQ65k8GZJMVZUyVX3TzPNe9ZwMN3dVx8R27OQAAADg0ByusVFVVaWVK1dq1apV8vHxsYy7urrK3d36KjsPDw9VVla2uM3exoabm6vVa7PZtZk9AWtkBfYgL7AVWYE9yMu/uLtbn4umV8lo/+OF3NFdIXd014h+vnrhwJlmFx13NZn08H99oAE9vTSgp7cG9PTSf/b0Un8/L3n981j/k3dZJy9e04EzRRp5R3e75vt9zEprL3YKCAhQfn5+s9slqaSkRJmZmY2aH7agvmia+a098po9s9G4S8FX6jbrMVX8fqdqYyd1wMwcB1mBPcgLbEVWYA/yAlt1RFYcrrGxadMm3XXXXRo3bpzVuK+vrz7//HOrsfLycrm5ubW4zV41NXVquMZgdXWd3Z+DzomswB7kBbYiK7AHeXEO/ye4lwL9vazu0PiW2UWqrTf0eVG5Pi8ql1Rktb2nl5tu9+2is0XlkqT9nxZq/JDbZEjq7ummPt262DSHtsiKyZ4FTG6xtrzYqaHExESFhIQ0qlFsQX3RhLo6+TyzWDKMRmvgmAxDhsmkLs8sUfF94yXXzv0fKp0+K7ALeYGtyArsQV5gq/auLxyusbF3716VlJQoNDRUklRZWan9+/erb9++qq2tteyXn5+v6upq+fr6atiwYVbPz/3uNgAAAMCRmaRvHsfzz7+3zQiRr6dZF4pv6H+LK3S+uEIXiiv0v8U39PWNGl2t+ObPt0pu2PcIq++rtrzY6bt27dqlkydPKisr69/6HPyL2/Fjcr10qdntJsOQ66V/yO34MdWEj23HmQEAAMBZOFxjIz093aqBsX79eo0YMUJTpkzRxIkTlZWVpcmTJ2vLli0aM2aMXF1dFRYWptLS0ia3AQAAAI7o20XHe3X10KRhvbUnt0CFpVXy93FXr64e6tfdU+ED/aze88bfLmn9oS+afISVq4tJK388uJ1m73huxcVOH3/8sdasWaNXXnlF/v7+bTFNSHIpLGjT/QAAAND5OFxjo3fv3lavvby81KNHD/n5+SkpKUmLFi3S+vXrVVdXp7S0NEmS2WxudhsAAADgiFqzyHncyNsV3Kdrk4+wSn14pIb06ryLiLd0sVNZWZk8PDzsunvjypUrmjt3rmbPnq3g4GCVl3/z6C9vb+9b9SV0GvW9et98Jzv2AwAAQOdjMoyGT3x1bIWFhcrNzdWoUaPk5+dn8zZbXblSavUMXHd3V54lB5uQFdiDvMBWZAX2IC+dQ15hqR5L+6jRI6z++9EQmxsbbZUVk0ny93ecZkp2drYWLVokb29vy8VOgYGBioyMVEJCgqKjo5t8X35+vqKionTmzBnLWGpqqtauXdto3+/uYwvqiybU1cnv7mC5fPWVTE2Uo4bJpPo+t6v4g0869RobZAX2IC+wFVmBPcgLbNUR9YXTNTZuNQoPtBZZgT3IC2xFVmAP8tI5FJZWaWbah40eYfX7R0epV1cPmz7j+9rYkNrmYqe2RH3RNPe331K3WY9JklVzw/jnipHXX/1vVcfEdsjcHAVZgT3IC2xFVmAP8gJb0dhwABQeaC2yAnuQF9iKrMAe5KXzqK6ttzzCyjCMmz7CqqHvc2PD0VBfNM/97bfk89xSq4XE627vq7IXkjt9U0MiK7APeYGtyArsQV5gq46oLxxujQ0AAAAALftuE8NkMsndbOrA2QCtUx0Tq+LxE+V2/JhcCgtU36u3an44plM/fgoAAAC2obEBAAAAAOgYrq6qCR/b0bMAAACAk7H9fnUAAAAAAAAAAIAORmMDAAAAAAAAAAA4DRobAAAAAAAAAADAadDYAAAAAAAAAAAAToPGBgAAAAAAAAAAcBo0NgAAAAAAAAAAgNOgsQEAAAAAAAAAAJwGjQ0AAAAAAAAAAOA0aGwAAAAAAAAAAACnQWMDAAAAAAAAAAA4DRobAAAAAAAAAADAadDYAAAAAAAAAAAAToPGBgAAAAAAAAAAcBo0NgAAAAAAAAAAgNOgsQEAAAAAAAAAAJyGuaMn4GhMJtvGgKaQFdiDvMBWZAX2IC+wVVtkhbzdHPUF/h1kBfYgL7AVWYE9yAts1d71hckwDOPfPyQAAAAAAAAAAMCtx6OoAAAAAAAAAACA06CxAQAAAAAAAAAAnAaNDQAAAAAAAAAA4DRobAAAAAAAAAAAAKdBYwMAAAAAAAAAADgNGhsAAAAAAAAAAMBp0NgAAAAAAAAAAABOg8YGAAAAAAAAAABwGjQ2AABwICUlJfrwww9VXFzc0VMBAAAA4OSoLwB8X9HYaMbZs2cVFxensLAwJScnyzCMjp4SHExJSYkiIyOVn59vGSM3aCg7O1tRUVEaOnSopk2bpnPnzkkiK2javn37dP/992v16tWKiIjQvn37JJEXtGzWrFnavXu3JLKCxpKSkhQUFGT5c99990kiKx2Bc46bob6ALagvYA/qC7QG9QVa4kj1BY2NJlRXV2vu3LkKDg7WG2+8oXPnzlm+oQFJKi4u1ty5c/WPf/zDMkZu0NCXX36phIQELVq0SIcPH9btt9+uZ599lqygSdevX1dSUpJ27typrKwsJSYm6sUXXyQvaNFbb72lv/zlL5L4PYSmffrpp9qyZYtOnDihEydO6M033yQrHYBzjpuhvoAtqC9gD+oLtAb1BW7GoeoLA428++67RlhYmFFRUWEYhmGcPn3amDFjRgfPCo5k5syZRmpqqjF48GDj4sWLhmGQGzR26NAhIz093fI6JyfHCA4OJito0qVLl4w9e/ZYXp8+fdoICQkhL2hWSUmJMWbMGOOBBx4w3njjDbKCRmpqaoyQkBCjrKzMapystD/OOW6G+gK2oL6APagvYC/qC9yMo9UX5lvfOnE+eXl5GjFihDw9PSVJQUFBlts7Aemb264CAgK0Zs0ayxi5QUMRERFWr8+fP6/+/fuTFTSpT58+io2NlSTV1NRo+/btuv/++8kLmpWcnKzo6GhVVVVJ4vcQGjtz5owMw9DkyZNVWFiosLAwJSUlkZUOwDnHzVBfwBbUF7AH9QXsRX2Bm3G0+oJHUTWhrKxM/fr1s7w2mUxycXHRtWvXOnBWcCQBAQGNxsgNWlJdXa3t27fr4YcfJitoUV5ensLDw3X06FElJCSQFzTp+PHjysnJ0ZIlSyxjZAUNnTt3ToGBgUpJSdG+ffvk5uamFStWkJUOwDnHzVBfwF7UF7AV9QVsQX0BWzhafUFjowmurq5yd3e3GvPw8FBlZWUHzQjOgNygJRs2bJCXl5cefPBBsoIWBQUFKTU1VYMGDdLy5cvJCxqpqqrSypUrtWrVKvn4+FjGyQoaio2N1euvv67hw4crICBAzz//vI4ePar6+nqy0s74/kRrkBu0hPoCtqK+wM1QX8BWjlZf0Nhogq+vr4qLi63GysvL5ebm1kEzgjMgN2jO0aNHlZGRoZSUFLm5uZEVtMhkMmno0KFat26dDh48SF7QyKZNm3TXXXdp3LhxVuNkBTfTrVs31dfXy9/fn6y0M74/0RrkBs2hvoA9qC9wM9QXaK2Ori9YY6MJw4YN065duyyv8/PzVV1dLV9f3w6cFRwduUFTLl68qMWLF2vVqlUaNGiQJLKCpuXk5Ojw4cNatmyZpG+ujpGkgQMHkhdY2bt3r0pKShQaGipJqqys1P79+9W3b1/V1tZa9iMrWLt2rUaMGKEJEyZIknJzc+Xi4qKgoCB+rrQzfvejNcgNmkJ9AVtRX8BW1BewlaPVF9yx0YSwsDCVlpYqKytLkrRlyxaNGTPG8ksAaAq5QUOVlZWaM2eOoqOjFRUVpfLycpWXlys0NJSsoJGBAwcqMzNTmZmZ+uqrr5SSkqLw8HCNGzeOvMBKenq69u7dq6ysLGVlZSkyMlJPP/200tLSyAqs3HnnndqwYYNOnDihnJwcJSUlacqUKQoPDycr7Yx/J6I1yA0aor6APagvYCvqC9jK0eoLk2EYxi09gpPKzs7WokWL5O3trbq6OqWlpSkwMLCjpwUHExQUpIMHD1oWyCE3+K7s7Gw9+eSTjcYPHjyovLw8soJGjhw5orVr16qgoED33nuvVq1aJT8/P362oEXPPPOM7rnnHk2dOpWsoJGUlBRlZGTI29tb0dHRWrhwoby8vMhKB+CcwxbUF2gJ9QXsRX2B1qC+QEscqb6gsdGCwsJC5ebmatSoUfLz8+vo6cBJkBvYiqzAHuQFtiIrsBVZaX+cc7QGuYGtyArsQV5gK7ICW7V3VmhsAAAAAAAAAAAAp8EaGwAAAAAAAAAAwGnQ2AAAAAAAAAAAAE6DxgYAAAAAAAAAAHAaNDYAAAAAAAAAAIDToLEBAGgzNTU1qq+vtxqrr69XdXW1qqurZRhGqz634WcCAAAA+P6jvgAANIfGBgCgzWzevFmjR49WZGSkYmJi9MADD2jkyJFKTk5WZGSkIiIiFBkZqeHDh+sPf/iD5X0VFRVat26dKisrJUkrVqzQb3/7W0nSxYsXNWnSJF29etXqWFu3blVJSYk2btyo1atXq7CwUJs2bZIkPfnkk3rvvffa6asGAAAAcCtQXwAAmkNjAwDQZubNm6eoqCh1795d/v7+8vPz06BBg5SQkKAFCxbo0KFDevfdd9WjRw9FRERY3mc2m3Xu3DnNnz9fNTU1MpvN6tKli2pra5WQkKC7775bPXv2tDqWi4uLnn/+ebm7u8tsNmvXrl2qra1VfX29/vrXv6pfv36N5vfYY48pNTVV0jdXf02dOlXLli27pecEAAAAQOtQXwAAmmPu6AkAAL5/HnzwQQUHB6uoqEgbN25UcXGxdu/erT179mjs2LEaPXq0/P39JUk3btyQ2WzWSy+9pNmzZ6u4uNjyOadPn5arq6uee+45y23oHh4eunbtmvr27au6ujpduHBB169fV319vQYOHKgTJ07Iy8tLP/jBDyRJdXV1qqurk7u7u9UcX3rpJZWXl2vFihXtd2IAAAAA2I36AgDQEI0NAECb27Fjh7y9vVVTUyM3NzfddtttSk9P14YNG5SSkqKlS5da9n3kkUd0+fJleXp6SpImTpyo0tJSde/eXa+99ppMJpPGjx+vmpoahYeH61e/+pWuX7+uP//5zyooKFBOTo7uvPNO9ejRQ7m5uTp79qwqKyt13333qaKiQhUVFZo1a5bmzZtnOWZOTo7S0tKUkZEhb2/vdj8/AAAAAGxHfQEAaIjGBgCgza1evVpBQUE6deqUEhMT9f7778vHx0cHDhzQ6tWrtWPHDn3xxRdauXKldu/ebXnfqVOn9NRTT2nw4MGKiopSfn6+KisrtXTpUvXo0cOyX0BAgOLj47VgwQJFRETIx8dH+fn5+s1vfqO4uDgtWbJE06ZN02uvvabTp09bFR1ff/21li5dqoULF2ro0KHtel4AAAAA2I/6AgDQEGtsAADaTGJiov70pz/p8ccfV1xcnDZt2qSqqipdvXpVTz31lOLj4xUYGKidO3dKklxdXSVJ5eXl2rhxo+bNm6eUlBQNHjxYtbW1+uUvfymTyaSJEyfqwIEDluMcO3ZMc+fOVVJSksLCwnTbbbdZrrb68ssvdeHCBUlSUVGRevXqZTXHHTt26PLly3Jzc2unswIAAACgNagvAADN4Y4NAECbmT59ulxdXXX9+nVVV1frypUrunLlirZu3arg4GB98cUXunr1qiRp7dq1kqSMjAxt2LBB48aN0549e+Tn56dDhw7Jx8dH3bp105o1a/TOO+/o2WefVf/+/TVkyBCFhoYqMzNTVVVVysjI0JtvvikfHx9t2rRJs2bN0gcffCBJys/P17hx46zm2K1bN/30pz/V5s2b9ZOf/EQeHh7teo4AAAAA2Ib6AgDQHO7YAAC0mSFDhuihhx5Sly5dFBcXp+3bt2v9+vVyd3fXiy++qDlz5uj8+fNasmSJSktLJUljxozRzp07tW7dOvn5+UmSPDw89OGHH1o+d8KECcrOztaAAQMkSdnZ2YqLi1NMTIzKyso0ZcoUhYaGqqioSIsWLVJBQYEuX76sDz74QKNGjbKa48yZM/XEE0+orq5OGRkZ7XRmAAAAANiL+gIA0Bzu2AAAtJmrV69q27Ztys3N1YQJE3TkyBFJ0qVLlzRlyhTV1NRo5syZSkxMlLu7uyRpxowZMpvNMpv/9SupoKBAkvTRRx9Zxmpra+Xp6akDBw5owoQJio6OVkxMjF599VX169dPsbGxmjRpkkwmk2JjY7V48WJ17dpVvXv3tpqj2WyWp6en4uPjtXXrVk2fPl1dunS51acGAAAAgJ2oLwAAzaGxAQBoExcvXtTkyZM1ceJE/e53v9P8+fPVvXt3xcTEaOzYsRo9erTWr1+vmpoa1dXVWd537Ngxq885ceKEli1bJl9fXz366KOKi4tr8njnz59Xnz59NH/+fPn7+0uSZbG+adOm6ZVXXlFCQkKz833ooYf06quvaufOnZo1a9a/++UDAAAAaEPUFwCAlvAoKgBAmwgICNDrr7+uYcOGafr06QoPD9e2bdvk4uIiwzAUGxur9PR0HTx4UD/84Q8VHx+vsrIyq884cuSIlixZosTERG3evFmpqalau3Zto/0kKSgoSDt27FBMTIwuXLigwMBAPfHEEyoqKtLy5cs1duxYbd68WZ999lmT8+3SpYvi4+O1bds2lZeX35JzAgAAAKB1qC8AAC0xGYZhdPQkAADfD59//rkWLFigZcuW6Uc/+pEkKTMzU++//75SUlIs+508eVJnz57VjBkzlJubq7/97W/at2+fysvLlZiYqNDQUElSeXm5XnjhBf3xj3/U+PHjFRERoaioKF29elUZGRnav3+/Ro0apcWLF8vHx0dbtmxRWlqa4uPj9bOf/UxpaWlKSUnRypUrNXny5I44JQAAAABaifoCANAcGhsAgA719NNPy83NTT/+8Y8VFRUlF5fGNxPm5eUpPT1dAwYM0M9//nMZhqH09HRFRUVZPeP2nXfe0bBhwxQQEGAZO3nypIKCgtS1a9d2+XoAAAAAdBzqCwDoHGhsAAAAAAAAAAAAp8EaGwAAAAAAAAAAwGnQ2AAAAAAAAAAAAE6DxgYAAAAAAAAAAHAaNDYAAAAAAAAAAIDToLEBAAAAAAAAAACcBo0NAAAAAAAAAADgNGhsAAAAAAAAAAAAp0FjAwAAAAAAAAAAOA0aGwAAAAAAAAAAwGn8fwdcOt0yQIOUAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 225
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T02:25:33.621843Z",
     "start_time": "2025-09-07T02:25:33.388505Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from scipy.optimize import minimize_scalar\n",
    "\n",
    "# 假设 processed_df 已存在且包含所需列\n",
    "features = ['年龄', '身高', '体重', 'bmi_final', '13_gc_final', '18_gc_final', '21_gc_final', 'ratio over 0.04','IVF妊娠_encoded']\n",
    "target_col = '最早达到目标Y染色体浓度的孕周'\n",
    "\n",
    "# 超参数设置\n",
    "lambda_weight = 1.0  # 准确性惩罚权重\n",
    "beta_weight = 10    # 延迟惩罚权重\n",
    "k_range = range(3, 7)  # K值搜索范围\n",
    "\n",
    "# 存储评估结果\n",
    "risk_scores = []\n",
    "sil_scores = []\n",
    "best_tg_per_k = {}\n",
    "clusters_per_k = {}\n",
    "\n",
    "# 动态调整K值的核心逻辑\n",
    "for k in k_range:\n",
    "    print(f\"\\nProcessing K={k}\")\n",
    "\n",
    "    # Step 1: 标准化特征\n",
    "    X = processed_df[features]\n",
    "    scaler = StandardScaler()\n",
    "    X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "    # Step 2: 第一次KMeans聚类\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "    kmeans.fit(X_scaled)\n",
    "    labels = kmeans.labels_\n",
    "    processed_df[f'first_cluster_{k}'] = labels\n",
    "\n",
    "    # Step 3: 二次分组与范围合并\n",
    "    all_ranges = []\n",
    "    for cluster_id in range(k):\n",
    "        cluster_mask = (labels == cluster_id)\n",
    "        cluster_data = processed_df[cluster_mask]\n",
    "\n",
    "        # 基于BMI的回归树分组\n",
    "        bmi_values = cluster_data['bmi_final'].values.reshape(-1, 1)\n",
    "        tree = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
    "        tree.fit(bmi_values, bmi_values)\n",
    "        leaves = tree.apply(bmi_values)\n",
    "        unique_leaves = np.unique(leaves)\n",
    "\n",
    "        # 提取BMI范围\n",
    "        ranges = []\n",
    "        for leaf in unique_leaves:\n",
    "            mask = (leaves == leaf).flatten()\n",
    "            min_bmi = bmi_values[mask].min()\n",
    "            max_bmi = bmi_values[mask].max()\n",
    "            ranges.append((min_bmi, max_bmi))\n",
    "        all_ranges.extend(ranges)\n",
    "\n",
    "    # 合并重叠范围\n",
    "    def merge_ranges(ranges):\n",
    "        if not ranges:\n",
    "            return []\n",
    "        sorted_ranges = sorted(ranges, key=lambda x: x[0])\n",
    "        merged = [sorted_ranges[0]]\n",
    "        for current in sorted_ranges[1:]:\n",
    "            last = merged[-1]\n",
    "            if current[0] <= last[1]:\n",
    "                merged[-1] = (last[0], max(last[1], current[1]))\n",
    "            else:\n",
    "                merged.append(current)\n",
    "        return merged\n",
    "\n",
    "    merged_ranges = merge_ranges(all_ranges)\n",
    "    print(f\"K={k} 合并后 BMI 范围数量: {len(merged_ranges)}\")\n",
    "\n",
    "    # Step 4: 分配最终组别\n",
    "    def assign_group(bmi_value, ranges):\n",
    "        for group_id, (start, end) in enumerate(ranges):\n",
    "            if start <= bmi_value <= end:\n",
    "                return group_id\n",
    "        return -1\n",
    "\n",
    "    processed_df[f'final_group_{k}'] = processed_df['bmi_final'].apply(\n",
    "        lambda x: assign_group(x, merged_ranges)\n",
    "    )\n",
    "\n",
    "    # Step 5: 风险计算\n",
    "    num = len(merged_ranges)\n",
    "    best_tg_list = []\n",
    "    total_risk = 0\n",
    "\n",
    "    def expected_risk(t_candidate, group_data):\n",
    "        risk = 0\n",
    "        for t_star_i in group_data:\n",
    "            accuracy_risk = lambda_weight if t_candidate < t_star_i else 0\n",
    "            delay_risk = beta_weight * max(0, t_candidate - 13)\n",
    "            risk += accuracy_risk + delay_risk\n",
    "        return risk / len(group_data)\n",
    "\n",
    "    for label in range(num):\n",
    "        group_data = processed_df[processed_df[f'final_group_{k}'] == label][target_col].values\n",
    "        if len(group_data) == 0:\n",
    "            continue\n",
    "\n",
    "        res = minimize_scalar(\n",
    "            lambda t: expected_risk(t, group_data),\n",
    "            bounds=(8, 25),\n",
    "            method='bounded'\n",
    "        )\n",
    "        best_tg = res.x\n",
    "        best_tg_list.append(best_tg)\n",
    "        total_risk += expected_risk(best_tg, group_data) * len(group_data)\n",
    "\n",
    "    avg_risk = total_risk / len(processed_df)\n",
    "    risk_scores.append(avg_risk)\n",
    "    best_tg_per_k[k] = best_tg_list\n",
    "    clusters_per_k[k] = processed_df[f'final_group_{k}'].values\n",
    "\n",
    "    # Step 6: 计算轮廓系数\n",
    "    sil_score = silhouette_score(X_scaled, labels)\n",
    "    sil_scores.append(sil_score)\n",
    "\n",
    "# Step 7: 综合评分与最优K选择\n",
    "risk_norm = (np.max(risk_scores) - np.array(risk_scores)) / (np.max(risk_scores) + 1e-8)\n",
    "combined_score = np.array(sil_scores) + 0.5 * risk_norm\n",
    "optimal_k = k_range[np.argmax(combined_score)]\n",
    "\n",
    "print(f\"\\nOptimal K: {optimal_k}\")\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(k_range, sil_scores, 'o-', label='轮廓系数')\n",
    "plt.plot(k_range, risk_norm, 's-', label='归一化风险下降')\n",
    "plt.plot(k_range, combined_score, 'd-', label='综合评分')\n",
    "plt.axvline(optimal_k, color='r', linestyle='--', label=f'最优k={optimal_k}')\n",
    "plt.xlabel('聚类数 k')\n",
    "plt.ylabel('得分')\n",
    "plt.legend()\n",
    "plt.title('聚类数选择')"
   ],
   "id": "7dbe1da9cdd2dd20",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Processing K=3\n",
      "K=3 合并后 BMI 范围数量: 3\n",
      "\n",
      "Processing K=4\n",
      "K=4 合并后 BMI 范围数量: 3\n",
      "\n",
      "Processing K=5\n",
      "K=5 合并后 BMI 范围数量: 4\n",
      "\n",
      "Processing K=6\n",
      "K=6 合并后 BMI 范围数量: 4\n",
      "\n",
      "Optimal K: 6\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '聚类数选择')"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 226
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T02:25:33.667974Z",
     "start_time": "2025-09-07T02:25:33.635065Z"
    }
   },
   "cell_type": "code",
   "source": [
    "best_k = 6\n",
    "# 假设 processed_df 已存在，且包含以下列\n",
    "features = ['年龄', '身高', '体重', 'bmi_final', '13_gc_final', '18_gc_final', '21_gc_final', 'ratio over 0.04','IVF妊娠_encoded']\n",
    "\n",
    "# Step 1: 标准化多尺度特征\n",
    "X = processed_df[features]\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "# Step 2: 第一次 KMeans 聚类\n",
    "kmeans = KMeans(n_clusters=best_k, random_state=42)\n",
    "kmeans.fit(X_scaled)\n",
    "processed_df['first_cluster'] = kmeans.labels_\n",
    "\n",
    "\n",
    "# Step 3: 对每个簇进行基于 BMI 的回归树二次分组\n",
    "def split_cluster_bmi(cluster_data):\n",
    "    \"\"\"\n",
    "    使用回归树对单个簇内的 BMI 数据进行二次分组\n",
    "    返回每个子簇的 BMI 范围（min, max）\n",
    "    \"\"\"\n",
    "    bmi_values = cluster_data['bmi_final'].values.reshape(-1, 1)\n",
    "\n",
    "    # 拟合回归树（根据 BMI 分割数据）\n",
    "    tree = DecisionTreeRegressor(max_depth=2, random_state=42)  # 控制树的深度\n",
    "    tree.fit(bmi_values, bmi_values)  # 特征和目标均为 BMI 值\n",
    "\n",
    "    # 获取每个样本所在的叶子节点 ID\n",
    "    leaves = tree.apply(bmi_values)\n",
    "    unique_leaves = np.unique(leaves)\n",
    "\n",
    "    # 统计每个叶子节点的 BMI 范围\n",
    "    ranges = []\n",
    "    for leaf in unique_leaves:\n",
    "        mask = (leaves == leaf).flatten()\n",
    "        min_bmi = bmi_values[mask].min()\n",
    "        max_bmi = bmi_values[mask].max()\n",
    "        ranges.append((min_bmi, max_bmi))\n",
    "\n",
    "    return ranges\n",
    "\n",
    "\n",
    "# Step 4: 合并重叠的 BMI 范围\n",
    "def merge_overlapping_ranges(ranges):\n",
    "    \"\"\"\n",
    "    合并所有二次分组后的 BMI 范围，消除重叠\n",
    "    \"\"\"\n",
    "    if not ranges:\n",
    "        return []\n",
    "    # 按起始 BMI 排序\n",
    "    sorted_ranges = sorted(ranges, key=lambda x: x[0])\n",
    "    merged = [sorted_ranges[0]]\n",
    "\n",
    "    for current in sorted_ranges[1:]:\n",
    "        last = merged[-1]\n",
    "        if current[0] <= last[1]:  # 存在重叠\n",
    "            merged[-1] = (last[0], max(last[1], current[1]))  # 合并范围\n",
    "        else:\n",
    "            merged.append(current)\n",
    "\n",
    "    return merged\n",
    "\n",
    "\n",
    "# Step 5: 执行二次分组与范围合并\n",
    "all_ranges = []\n",
    "for cluster_id in range(kmeans.n_clusters):\n",
    "    cluster_mask = (processed_df['first_cluster'] == cluster_id)\n",
    "    cluster_data = processed_df[cluster_mask]\n",
    "    ranges = split_cluster_bmi(cluster_data)\n",
    "    all_ranges.extend(ranges)\n",
    "\n",
    "# 合并所有子簇的 BMI 范围\n",
    "merged_ranges = merge_overlapping_ranges(all_ranges)\n",
    "print(\"合并后的 BMI 范围:\", merged_ranges)\n",
    "\n",
    "# Step 6: 将样本分配到最终的合并后的 BMI 组中\n",
    "def assign_to_merged_group(bmi_value, merged_ranges):\n",
    "    \"\"\"\n",
    "    根据合并后的 BMI 范围，为每个样本分配最终组别\n",
    "    \"\"\"\n",
    "    for group_id, (start, end) in enumerate(merged_ranges):\n",
    "        if start <= bmi_value <= end:\n",
    "            return group_id\n",
    "    return -1  # 不属于任何组\n",
    "\n",
    "processed_df['final_group'] = processed_df['bmi_final'].apply(lambda x: assign_to_merged_group(x, merged_ranges))\n",
    "processed_df"
   ],
   "id": "861c75fb8289cc8a",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "合并后的 BMI 范围: [(np.float64(20.703125), np.float64(20.703125)), (np.float64(27.101577872500002), np.float64(40.5709342575)), (np.float64(42.67578125), np.float64(43.708714055)), (np.float64(44.98269896), np.float64(45.71428571))]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "     孕妇代码  最早达到目标Y染色体浓度的孕周        年龄        身高        体重  ratio over 0.04  \\\n",
       "0    A001        20.142857  0.454545  0.516129  0.229885              0.5   \n",
       "1    A002        13.857143  0.500000  0.161290  0.241379              1.0   \n",
       "2    A003        13.000000  0.636364  0.516129  0.295402              1.0   \n",
       "3    A004        11.000000  0.227273  0.451613  0.212644              1.0   \n",
       "4    A005        12.285714  0.409091  0.193548  0.165517              1.0   \n",
       "..    ...              ...       ...       ...       ...              ...   \n",
       "255  A263        12.571429  0.409091  0.419355  0.222299              1.0   \n",
       "256  A264        11.714286  0.409091  0.870968  0.482184              1.0   \n",
       "257  A265        11.714286  0.500000  0.774194  0.484713              1.0   \n",
       "258  A266        13.714286  0.409091  0.483871  0.324598              1.0   \n",
       "259  A267        11.571429  0.318182  0.354839  0.238621              1.0   \n",
       "\n",
       "     IVF妊娠_encoded  bmi_final  13_gc_final  18_gc_final  ...  first_cluster_3  \\\n",
       "0                0  28.710938     0.376531     0.389917  ...                2   \n",
       "1                0  33.962434     0.379446     0.392051  ...                2   \n",
       "2                0  31.226562     0.380396     0.393254  ...                0   \n",
       "3                0  28.721359     0.380350     0.393046  ...                2   \n",
       "4                0  31.077778     0.376861     0.389487  ...                2   \n",
       "..             ...        ...          ...          ...  ...              ...   \n",
       "255              0  29.643738     0.379104     0.388323  ...                2   \n",
       "256              0  32.897314     0.379583     0.392359  ...                0   \n",
       "257              0  34.206054     0.377312     0.390401  ...                0   \n",
       "258              0  32.459274     0.377345     0.390744  ...                2   \n",
       "259              0  31.052210     0.378719     0.389932  ...                2   \n",
       "\n",
       "     final_group_3  first_cluster_4  final_group_4  first_cluster_5  \\\n",
       "0                1                2              1                4   \n",
       "1                1                2              1                2   \n",
       "2                1                0              1                2   \n",
       "3                1                2              1                0   \n",
       "4                1                2              1                4   \n",
       "..             ...              ...            ...              ...   \n",
       "255              1                2              1                4   \n",
       "256              1                0              1                0   \n",
       "257              1                0              1                1   \n",
       "258              1                2              1                4   \n",
       "259              1                2              1                2   \n",
       "\n",
       "     final_group_5  first_cluster_6  final_group_6  first_cluster  final_group  \n",
       "0                1                4              1              4            1  \n",
       "1                1                2              1              2            1  \n",
       "2                1                0              1              0            1  \n",
       "3                1                2              1              2            1  \n",
       "4                1                4              1              4            1  \n",
       "..             ...              ...            ...            ...          ...  \n",
       "255              1                4              1              4            1  \n",
       "256              1                0              1              0            1  \n",
       "257              1                1              1              1            1  \n",
       "258              1                4              1              4            1  \n",
       "259              1                2              1              2            1  \n",
       "\n",
       "[260 rows x 21 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>孕妇代码</th>\n",
       "      <th>最早达到目标Y染色体浓度的孕周</th>\n",
       "      <th>年龄</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>ratio over 0.04</th>\n",
       "      <th>IVF妊娠_encoded</th>\n",
       "      <th>bmi_final</th>\n",
       "      <th>13_gc_final</th>\n",
       "      <th>18_gc_final</th>\n",
       "      <th>...</th>\n",
       "      <th>first_cluster_3</th>\n",
       "      <th>final_group_3</th>\n",
       "      <th>first_cluster_4</th>\n",
       "      <th>final_group_4</th>\n",
       "      <th>first_cluster_5</th>\n",
       "      <th>final_group_5</th>\n",
       "      <th>first_cluster_6</th>\n",
       "      <th>final_group_6</th>\n",
       "      <th>first_cluster</th>\n",
       "      <th>final_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A001</td>\n",
       "      <td>20.142857</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>0.229885</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>28.710938</td>\n",
       "      <td>0.376531</td>\n",
       "      <td>0.389917</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A002</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.161290</td>\n",
       "      <td>0.241379</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>33.962434</td>\n",
       "      <td>0.379446</td>\n",
       "      <td>0.392051</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A003</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>0.295402</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.226562</td>\n",
       "      <td>0.380396</td>\n",
       "      <td>0.393254</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A004</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.451613</td>\n",
       "      <td>0.212644</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.721359</td>\n",
       "      <td>0.380350</td>\n",
       "      <td>0.393046</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A005</td>\n",
       "      <td>12.285714</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.193548</td>\n",
       "      <td>0.165517</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.077778</td>\n",
       "      <td>0.376861</td>\n",
       "      <td>0.389487</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>A263</td>\n",
       "      <td>12.571429</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.419355</td>\n",
       "      <td>0.222299</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.643738</td>\n",
       "      <td>0.379104</td>\n",
       "      <td>0.388323</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>A264</td>\n",
       "      <td>11.714286</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.870968</td>\n",
       "      <td>0.482184</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.897314</td>\n",
       "      <td>0.379583</td>\n",
       "      <td>0.392359</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>A265</td>\n",
       "      <td>11.714286</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.774194</td>\n",
       "      <td>0.484713</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.206054</td>\n",
       "      <td>0.377312</td>\n",
       "      <td>0.390401</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>A266</td>\n",
       "      <td>13.714286</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.483871</td>\n",
       "      <td>0.324598</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.459274</td>\n",
       "      <td>0.377345</td>\n",
       "      <td>0.390744</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>0.318182</td>\n",
       "      <td>0.354839</td>\n",
       "      <td>0.238621</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.052210</td>\n",
       "      <td>0.378719</td>\n",
       "      <td>0.389932</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>260 rows × 21 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 227
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T02:25:33.737853Z",
     "start_time": "2025-09-07T02:25:33.722182Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 假设 target_col 是 '最早达到目标Y染色体浓度的孕周'\n",
    "target_col = '最早达到目标Y染色体浓度的孕周'\n",
    "# 存储每个组的推荐检测时点\n",
    "best_t_per_group = {}\n",
    "# 遍历每个最终分组\n",
    "for group_id in range(len(merged_ranges)):\n",
    "    # 提取当前组的数据\n",
    "    group_mask = (processed_df['final_group'] == group_id)\n",
    "    group_tstars = processed_df[group_mask][target_col].values\n",
    "    if len(group_tstars) == 0:\n",
    "        print(f\"Group {group_id} is empty, skipping optimization.\")\n",
    "        continue\n",
    "    # 定义惩罚函数\n",
    "    def risk_func(t):\n",
    "        total = 0\n",
    "        for t_star_i in group_tstars:\n",
    "            acc_risk = 10.0 if t < t_star_i else 0\n",
    "            delay_risk = 1.0 * max(0, t - 13)\n",
    "            total += acc_risk + delay_risk\n",
    "        return total / len(group_tstars)\n",
    "    # 寻找最优检测时点\n",
    "    res = minimize_scalar(risk_func, bounds=(10, 25), method='bounded')\n",
    "    best_t = res.x\n",
    "    best_t_per_group[group_id] = best_t\n",
    "    print(f\"Group {group_id} 最佳检测时点: {best_t:.2f} 周\")\n",
    "# 将推荐时点映射回原数据\n",
    "def assign_best_t(row):\n",
    "    group_id = row['final_group']\n",
    "    if group_id in best_t_per_group:\n",
    "        return best_t_per_group[group_id]\n",
    "    return np.nan  # 无法匹配的情况\n",
    "processed_df['推荐检测时点'] = processed_df.apply(assign_best_t, axis=1)\n",
    "\n",
    "# 查看最终结果\n",
    "print(\"\\n最终分组与推荐检测时点:\")\n",
    "for group_id, best_t in best_t_per_group.items():\n",
    "    print(f\"Group {group_id}: BMI范围 {merged_ranges[group_id]} → 推荐时点 {best_t:.2f} 周\")"
   ],
   "id": "b17e9e1e753a6398",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Group 0 最佳检测时点: 16.29 周\n",
      "Group 1 最佳检测时点: 13.86 周\n",
      "Group 2 最佳检测时点: 12.71 周\n",
      "Group 3 最佳检测时点: 11.68 周\n",
      "\n",
      "最终分组与推荐检测时点:\n",
      "Group 0: BMI范围 (np.float64(20.703125), np.float64(20.703125)) → 推荐时点 16.29 周\n",
      "Group 1: BMI范围 (np.float64(27.101577872500002), np.float64(40.5709342575)) → 推荐时点 13.86 周\n",
      "Group 2: BMI范围 (np.float64(42.67578125), np.float64(43.708714055)) → 推荐时点 12.71 周\n",
      "Group 3: BMI范围 (np.float64(44.98269896), np.float64(45.71428571)) → 推荐时点 11.68 周\n"
     ]
    }
   ],
   "execution_count": 228
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T02:25:33.883513Z",
     "start_time": "2025-09-07T02:25:33.870659Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "# 初始化结果列表\n",
    "summary_data = []\n",
    "# 遍历每个分组\n",
    "for group_id in range(len(merged_ranges)):\n",
    "    # 获取BMI范围\n",
    "    bmi_range = merged_ranges[group_id]\n",
    "    # 获取该组的样本数据\n",
    "    group_mask = (processed_df['final_group'] == group_id)\n",
    "    group_data = processed_df[group_mask]\n",
    "    # 计算BMI中值\n",
    "    bmi_median = group_data['bmi_final'].median() if not group_data.empty else np.nan\n",
    "    # 计算BMI均值\n",
    "    bmi_mean = group_data['bmi_final'].mean() if not group_data.empty else np.nan\n",
    "    # 获取推荐时点\n",
    "    best_t = best_t_per_group.get(group_id, np.nan)\n",
    "    # 统计孕妇数量\n",
    "    num_patients = len(group_data)\n",
    "    # 添加到结果列表\n",
    "    summary_data.append({\n",
    "        '组ID': group_id,\n",
    "        'BMI范围': bmi_range,\n",
    "        'BMI中值': bmi_median,\n",
    "        'BMI均值': bmi_mean,\n",
    "        '孕妇数量': num_patients,\n",
    "        '推荐检测时点': best_t\n",
    "    })\n",
    "# 转换为DataFrame\n",
    "group_summary_df = pd.DataFrame(summary_data)\n",
    "group_summary_df"
   ],
   "id": "62b616665b57484f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   组ID                                BMI范围      BMI中值      BMI均值  孕妇数量  \\\n",
       "0    0               (20.703125, 20.703125)  20.703125  20.703125     1   \n",
       "1    1  (27.101577872500002, 40.5709342575)  31.637417  32.134267   255   \n",
       "2    2          (42.67578125, 43.708714055)  43.192248  43.192248     2   \n",
       "3    3           (44.98269896, 45.71428571)  45.348492  45.348492     2   \n",
       "\n",
       "      推荐检测时点  \n",
       "0  16.285719  \n",
       "1  13.857144  \n",
       "2  12.705105  \n",
       "3  11.684412  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>组ID</th>\n",
       "      <th>BMI范围</th>\n",
       "      <th>BMI中值</th>\n",
       "      <th>BMI均值</th>\n",
       "      <th>孕妇数量</th>\n",
       "      <th>推荐检测时点</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>(20.703125, 20.703125)</td>\n",
       "      <td>20.703125</td>\n",
       "      <td>20.703125</td>\n",
       "      <td>1</td>\n",
       "      <td>16.285719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>(27.101577872500002, 40.5709342575)</td>\n",
       "      <td>31.637417</td>\n",
       "      <td>32.134267</td>\n",
       "      <td>255</td>\n",
       "      <td>13.857144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>(42.67578125, 43.708714055)</td>\n",
       "      <td>43.192248</td>\n",
       "      <td>43.192248</td>\n",
       "      <td>2</td>\n",
       "      <td>12.705105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>(44.98269896, 45.71428571)</td>\n",
       "      <td>45.348492</td>\n",
       "      <td>45.348492</td>\n",
       "      <td>2</td>\n",
       "      <td>11.684412</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 229
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T02:25:34.211338Z",
     "start_time": "2025-09-07T02:25:34.187226Z"
    }
   },
   "cell_type": "code",
   "source": "processed_df",
   "id": "b48d4cc1f3431b1c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     孕妇代码  最早达到目标Y染色体浓度的孕周        年龄        身高        体重  ratio over 0.04  \\\n",
       "0    A001        20.142857  0.454545  0.516129  0.229885              0.5   \n",
       "1    A002        13.857143  0.500000  0.161290  0.241379              1.0   \n",
       "2    A003        13.000000  0.636364  0.516129  0.295402              1.0   \n",
       "3    A004        11.000000  0.227273  0.451613  0.212644              1.0   \n",
       "4    A005        12.285714  0.409091  0.193548  0.165517              1.0   \n",
       "..    ...              ...       ...       ...       ...              ...   \n",
       "255  A263        12.571429  0.409091  0.419355  0.222299              1.0   \n",
       "256  A264        11.714286  0.409091  0.870968  0.482184              1.0   \n",
       "257  A265        11.714286  0.500000  0.774194  0.484713              1.0   \n",
       "258  A266        13.714286  0.409091  0.483871  0.324598              1.0   \n",
       "259  A267        11.571429  0.318182  0.354839  0.238621              1.0   \n",
       "\n",
       "     IVF妊娠_encoded  bmi_final  13_gc_final  18_gc_final  ...  final_group_3  \\\n",
       "0                0  28.710938     0.376531     0.389917  ...              1   \n",
       "1                0  33.962434     0.379446     0.392051  ...              1   \n",
       "2                0  31.226562     0.380396     0.393254  ...              1   \n",
       "3                0  28.721359     0.380350     0.393046  ...              1   \n",
       "4                0  31.077778     0.376861     0.389487  ...              1   \n",
       "..             ...        ...          ...          ...  ...            ...   \n",
       "255              0  29.643738     0.379104     0.388323  ...              1   \n",
       "256              0  32.897314     0.379583     0.392359  ...              1   \n",
       "257              0  34.206054     0.377312     0.390401  ...              1   \n",
       "258              0  32.459274     0.377345     0.390744  ...              1   \n",
       "259              0  31.052210     0.378719     0.389932  ...              1   \n",
       "\n",
       "     first_cluster_4  final_group_4  first_cluster_5  final_group_5  \\\n",
       "0                  2              1                4              1   \n",
       "1                  2              1                2              1   \n",
       "2                  0              1                2              1   \n",
       "3                  2              1                0              1   \n",
       "4                  2              1                4              1   \n",
       "..               ...            ...              ...            ...   \n",
       "255                2              1                4              1   \n",
       "256                0              1                0              1   \n",
       "257                0              1                1              1   \n",
       "258                2              1                4              1   \n",
       "259                2              1                2              1   \n",
       "\n",
       "     first_cluster_6  final_group_6  first_cluster  final_group     推荐检测时点  \n",
       "0                  4              1              4            1  13.857144  \n",
       "1                  2              1              2            1  13.857144  \n",
       "2                  0              1              0            1  13.857144  \n",
       "3                  2              1              2            1  13.857144  \n",
       "4                  4              1              4            1  13.857144  \n",
       "..               ...            ...            ...          ...        ...  \n",
       "255                4              1              4            1  13.857144  \n",
       "256                0              1              0            1  13.857144  \n",
       "257                1              1              1            1  13.857144  \n",
       "258                4              1              4            1  13.857144  \n",
       "259                2              1              2            1  13.857144  \n",
       "\n",
       "[260 rows x 22 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>孕妇代码</th>\n",
       "      <th>最早达到目标Y染色体浓度的孕周</th>\n",
       "      <th>年龄</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>ratio over 0.04</th>\n",
       "      <th>IVF妊娠_encoded</th>\n",
       "      <th>bmi_final</th>\n",
       "      <th>13_gc_final</th>\n",
       "      <th>18_gc_final</th>\n",
       "      <th>...</th>\n",
       "      <th>final_group_3</th>\n",
       "      <th>first_cluster_4</th>\n",
       "      <th>final_group_4</th>\n",
       "      <th>first_cluster_5</th>\n",
       "      <th>final_group_5</th>\n",
       "      <th>first_cluster_6</th>\n",
       "      <th>final_group_6</th>\n",
       "      <th>first_cluster</th>\n",
       "      <th>final_group</th>\n",
       "      <th>推荐检测时点</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A001</td>\n",
       "      <td>20.142857</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>0.229885</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>28.710938</td>\n",
       "      <td>0.376531</td>\n",
       "      <td>0.389917</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>13.857144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A002</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.161290</td>\n",
       "      <td>0.241379</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>33.962434</td>\n",
       "      <td>0.379446</td>\n",
       "      <td>0.392051</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13.857144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A003</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>0.295402</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.226562</td>\n",
       "      <td>0.380396</td>\n",
       "      <td>0.393254</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.857144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A004</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.451613</td>\n",
       "      <td>0.212644</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.721359</td>\n",
       "      <td>0.380350</td>\n",
       "      <td>0.393046</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13.857144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A005</td>\n",
       "      <td>12.285714</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.193548</td>\n",
       "      <td>0.165517</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.077778</td>\n",
       "      <td>0.376861</td>\n",
       "      <td>0.389487</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>13.857144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>A263</td>\n",
       "      <td>12.571429</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.419355</td>\n",
       "      <td>0.222299</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.643738</td>\n",
       "      <td>0.379104</td>\n",
       "      <td>0.388323</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>13.857144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>A264</td>\n",
       "      <td>11.714286</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.870968</td>\n",
       "      <td>0.482184</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.897314</td>\n",
       "      <td>0.379583</td>\n",
       "      <td>0.392359</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.857144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>A265</td>\n",
       "      <td>11.714286</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.774194</td>\n",
       "      <td>0.484713</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.206054</td>\n",
       "      <td>0.377312</td>\n",
       "      <td>0.390401</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13.857144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>A266</td>\n",
       "      <td>13.714286</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.483871</td>\n",
       "      <td>0.324598</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.459274</td>\n",
       "      <td>0.377345</td>\n",
       "      <td>0.390744</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>13.857144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>0.318182</td>\n",
       "      <td>0.354839</td>\n",
       "      <td>0.238621</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.052210</td>\n",
       "      <td>0.378719</td>\n",
       "      <td>0.389932</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13.857144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>260 rows × 22 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 230
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T02:25:51.361529Z",
     "start_time": "2025-09-07T02:25:51.253978Z"
    }
   },
   "cell_type": "code",
   "source": [
    "colors = plt.cm.Set1(np.linspace(0, 1, 5))\n",
    "custom_colors = ['#FF5733', '#33FF57', '#3357FF', '#F7B00E']  # 示例颜色\n",
    "labels_opt = processed_df['final_group']\n",
    "colors = [custom_colors[label] for label in labels_opt]\n",
    "scatter = plt.scatter(processed_df['bmi_final'], processed_df['最早达到目标Y染色体浓度的孕周'],c=colors)\n",
    "plt.xlabel('孕妇BMI')\n",
    "plt.ylabel('估计达标时间 t* (周)')\n",
    "plt.title('BMI vs 最早达标时间（聚类结果）')\n",
    "\n",
    "from matplotlib.patches import Patch\n",
    "# 生成图例标签\n",
    "legend_labels = [f'组{idx+1}' for idx in range(4)]\n",
    "# 创建颜色对应的颜色块\n",
    "legend_elements = [\n",
    "    Patch(facecolor=custom_colors[i], label=f'组{i+1}') for i in range(4)\n",
    "]\n",
    "# 添加图例\n",
    "plt.legend(handles=legend_elements, title=\"聚类分组\", loc='best')\n",
    "\n",
    "plt.show()"
   ],
   "id": "9af410da784f1a7a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 232
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T02:25:51.384792Z",
     "start_time": "2025-09-07T02:25:51.371416Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 计算达标率\n",
    "def calculate_compliance_rate(group_df, recommendation_time):\n",
    "    compliant = group_df[group_df['最早达到目标Y染色体浓度的孕周'] <= recommendation_time].shape[0]\n",
    "    total = group_df.shape[0]\n",
    "    return compliant / total if total > 0 else 0.0\n",
    "\n",
    "# 按组计算达标率\n",
    "compliance_rates = []\n",
    "for idx, row in group_summary_df.iterrows():\n",
    "    group_id = row.name  # 组号\n",
    "    group_data = processed_df[processed_df['final_group'] == group_id]\n",
    "    recommendation_time = row['推荐检测时点']\n",
    "\n",
    "    compliance_rate = calculate_compliance_rate(group_data, recommendation_time)\n",
    "    compliance_rates.append({\n",
    "        'BMI区间': row['BMI范围'],\n",
    "        '推荐检测时间(周)': recommendation_time,\n",
    "        '达标率': compliance_rate,\n",
    "        '组内人数': row['孕妇数量']\n",
    "    })\n",
    "\n",
    "# 生成达标率表\n",
    "compliance_df = pd.DataFrame(compliance_rates)\n",
    "compliance_df"
   ],
   "id": "b8478d6c41c067de",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                                 BMI区间  推荐检测时间(周)       达标率  组内人数\n",
       "0               (20.703125, 20.703125)  16.285719  1.000000     1\n",
       "1  (27.101577872500002, 40.5709342575)  13.857144  0.831373   255\n",
       "2          (42.67578125, 43.708714055)  12.705105  0.500000     2\n",
       "3           (44.98269896, 45.71428571)  11.684412  0.000000     2"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BMI区间</th>\n",
       "      <th>推荐检测时间(周)</th>\n",
       "      <th>达标率</th>\n",
       "      <th>组内人数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(20.703125, 20.703125)</td>\n",
       "      <td>16.285719</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(27.101577872500002, 40.5709342575)</td>\n",
       "      <td>13.857144</td>\n",
       "      <td>0.831373</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(42.67578125, 43.708714055)</td>\n",
       "      <td>12.705105</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(44.98269896, 45.71428571)</td>\n",
       "      <td>11.684412</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 233
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T02:25:51.550668Z",
     "start_time": "2025-09-07T02:25:51.426751Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 绘制柱状图\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(range(4), compliance_df['达标率'], color='skyblue')\n",
    "plt.xlabel('BMI区间')\n",
    "plt.ylabel('达标率')\n",
    "plt.title('各BMI区间推荐检测时间下的达标率')\n",
    "plt.grid(alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "fe554089715b9978",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 234
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T02:25:51.596002Z",
     "start_time": "2025-09-07T02:25:51.587567Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from scipy.stats import kruskal\n",
    "import numpy as np\n",
    "# 按组提取数据\n",
    "groups = [processed_df[processed_df['final_group'] == i]['最早达到目标Y染色体浓度的孕周'].values for i in range(4)]\n",
    "\n",
    "# 执行Kruskal-Wallis H检验\n",
    "stat, p_value = kruskal(*groups)\n",
    "\n",
    "print(f\"Kruskal-Wallis H 检验: H = {stat:.4f}, p = {p_value:.4f}\")\n",
    "if p_value<=0.05:\n",
    "    print('不同BMI组的最早达标时间存在显著差异 → 推荐分组策略合理。')\n",
    "else:\n",
    "    print('分组无统计学意义 → 需重新审视分组逻辑。')"
   ],
   "id": "9de3dd388236f069",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kruskal-Wallis H 检验: H = 7.7535, p = 0.0514\n",
      "分组无统计学意义 → 需重新审视分组逻辑。\n"
     ]
    }
   ],
   "execution_count": 235
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T02:25:51.641929Z",
     "start_time": "2025-09-07T02:25:51.637925Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 计算效应大小 (eta squared)\n",
    "all_data = np.concatenate(groups)\n",
    "group_means = [np.mean(group) for group in groups]\n",
    "overall_mean = np.mean(all_data)\n",
    "n = len(all_data)\n",
    "k = len(groups)\n",
    "SS_between = sum([len(group) * (mean - overall_mean)**2 for group, mean in zip(groups, group_means)])\n",
    "SS_total = sum((data - overall_mean)**2 for data in all_data)\n",
    "eta_squared = SS_between / SS_total\n",
    "print(f'Effect size (eta squared): {eta_squared:.4f}')"
   ],
   "id": "da02984e205b9303",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Effect size (eta squared): 0.1007\n"
     ]
    }
   ],
   "execution_count": 236
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T02:25:51.950Z",
     "start_time": "2025-09-07T02:25:51.662960Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 箱线图 + 散点图\n",
    "sns.boxplot(x='final_group', y='最早达到目标Y染色体浓度的孕周', data=processed_df)\n",
    "sns.stripplot(x='final_group', y='最早达到目标Y染色体浓度的孕周', data=processed_df, color='black', alpha=0.5)\n",
    "\n",
    "plt.title(f'分组趋势（p={p_value:.4f}, $\\\\eta^2$={eta_squared:.1f}）')\n",
    "plt.show()\n",
    "\n",
    "# 小提琴图\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.violinplot(x='final_group', y='最早达到目标Y染色体浓度的孕周', data=processed_df)\n",
    "plt.title('Violin Plot of 最早达到目标Y染色体浓度的孕周 by final_group')\n",
    "plt.show()"
   ],
   "id": "212a671af0cc1e59",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 237
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T02:25:52.096657Z",
     "start_time": "2025-09-07T02:25:51.968556Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# DCA决策分析\n",
    "# 1. 数据预处理\n",
    "processed_df['达标'] = (processed_df['推荐检测时点'] >= processed_df['最早达到目标Y染色体浓度的孕周']).astype(int)\n",
    "y_true = processed_df['达标'].values\n",
    "N = len(y_true)\n",
    "\n",
    "# 2. 定义计算净获益的函数\n",
    "def calculate_net_benefit(y_true, y_pred, theta):\n",
    "    TP = np.sum((y_pred >= theta) & (y_true == 1))\n",
    "    FP = np.sum((y_pred >= theta) & (y_true == 0))\n",
    "    P = np.sum(y_true == 1)\n",
    "    N_total = len(y_true)\n",
    "    net_benefit = (TP - (FP * theta / (1 - theta))) / (N_total - P)\n",
    "    return net_benefit\n",
    "\n",
    "# 3. 生成阈值范围\n",
    "thresholds = np.linspace(0.01, 1, 100)  # 避免除以0，从0.01开始\n",
    "\n",
    "# 4. 计算推荐策略的净获益\n",
    "net_benefits = []\n",
    "for theta in thresholds:\n",
    "    nb = calculate_net_benefit(y_true, y_true, theta)  # y_pred = y_true（推荐策略）\n",
    "    net_benefits.append(nb)\n",
    "\n",
    "# 5. 计算基线策略的净获益\n",
    "# 基线1：所有人都不检测（净获益 = 0）\n",
    "baseline_0 = [0] * len(thresholds)\n",
    "\n",
    "# 基线2：所有人都检测（净获益 = (TP - FP * theta) / (N - P)）\n",
    "net_benefits_all = []\n",
    "for theta in thresholds:\n",
    "    TP_all = np.sum(y_true == 1)  # 所有预测为1时，TP = 真实正例数\n",
    "    FP_all = np.sum(y_true == 0)  # 所有预测为1时，FP = 真实负例数\n",
    "    nb_all = (TP_all - (FP_all * theta / (1 - theta))) / (N - np.sum(y_true == 1))\n",
    "    net_benefits_all.append(nb_all)\n",
    "\n",
    "# 6. 绘制DCA曲线\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(thresholds, net_benefits, label='推荐策略', color='blue')\n",
    "plt.plot(thresholds, baseline_0, 'k--', label='不检测')  # 基线1\n",
    "plt.plot(thresholds, net_benefits_all, 'r--', label='统一检测')  # 基线2\n",
    "plt.xlabel('阈值概率')\n",
    "plt.ylabel('净获益')\n",
    "plt.title('决策曲线分析（DCA）')\n",
    "plt.ylim(-20, 8)\n",
    "plt.legend()\n",
    "plt.grid(alpha=0.3)\n",
    "plt.show()"
   ],
   "id": "a972967ca8fa61c1",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\20900\\AppData\\Local\\Temp\\ipykernel_17884\\2780224100.py:13: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  net_benefit = (TP - (FP * theta / (1 - theta))) / (N_total - P)\n",
      "C:\\Users\\20900\\AppData\\Local\\Temp\\ipykernel_17884\\2780224100.py:34: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  nb_all = (TP_all - (FP_all * theta / (1 - theta))) / (N - np.sum(y_true == 1))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 238
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T02:25:53.651720Z",
     "start_time": "2025-09-07T02:25:52.117767Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scipy.optimize import minimize_scalar\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 固定随机种子以保证可重复性\n",
    "np.random.seed(42)\n",
    "\n",
    "# 原始数据\n",
    "original_data = processed_df.copy()\n",
    "\n",
    "# 存储每次模拟的结果\n",
    "results = {\n",
    "    'best_t_per_group': [],\n",
    "    'merged_ranges': [],\n",
    "    'group_summary': []\n",
    "}\n",
    "\n",
    "# 设置噪声强度（标准差的比例）\n",
    "noise_level = 0.1  # 10% 的标准差噪声\n",
    "\n",
    "# 蒙特卡洛模拟（100次）\n",
    "for i in range(100):\n",
    "    # Step 1: 向原始数据添加高斯噪声\n",
    "    # 假设已标准化后的特征尺度一致\n",
    "    features = ['年龄', '身高', '体重', 'bmi_final', '13_gc_final', '18_gc_final', '21_gc_final', 'IVF妊娠_encoded', 'ratio over 0.04']\n",
    "    X = original_data[features].values\n",
    "    feature_std = X.std(axis=0)  # 每个特征的标准差\n",
    "    noise = np.random.randn(*X.shape) * noise_level * feature_std  # 高斯噪声\n",
    "    noisy_data = X + noise  # 添加噪声后的数据\n",
    "\n",
    "    # 重建DataFrame\n",
    "    noisy_df = original_data.copy()\n",
    "    noisy_df[features] = noisy_data\n",
    "\n",
    "    # Step 2: 多尺度聚类与二次分组\n",
    "    target_col = '最早达到目标Y染色体浓度的孕周'\n",
    "\n",
    "    X = noisy_df[features]\n",
    "    scaler = StandardScaler()\n",
    "    X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "    kmeans = KMeans(n_clusters=4, random_state=42)\n",
    "    kmeans.fit(X_scaled)\n",
    "    noisy_df['first_cluster'] = kmeans.labels_\n",
    "\n",
    "    def split_cluster_bmi(cluster_data):\n",
    "        bmi_values = cluster_data['bmi_final'].values.reshape(-1, 1)\n",
    "        tree = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
    "        tree.fit(bmi_values, bmi_values)\n",
    "        leaves = tree.apply(bmi_values)\n",
    "        unique_leaves = np.unique(leaves)\n",
    "\n",
    "        ranges = []\n",
    "        for leaf in unique_leaves:\n",
    "            mask = (leaves == leaf).flatten()\n",
    "            min_bmi = bmi_values[mask].min()\n",
    "            max_bmi = bmi_values[mask].max()\n",
    "            ranges.append((min_bmi, max_bmi))\n",
    "        return ranges\n",
    "\n",
    "    all_ranges = []\n",
    "    for cluster_id in range(kmeans.n_clusters):\n",
    "        cluster_mask = (noisy_df['first_cluster'] == cluster_id)\n",
    "        cluster_data = noisy_df[cluster_mask]\n",
    "        ranges = split_cluster_bmi(cluster_data)\n",
    "        all_ranges.extend(ranges)\n",
    "\n",
    "    def merge_ranges(ranges):\n",
    "        if not ranges:\n",
    "            return []\n",
    "        sorted_ranges = sorted(ranges, key=lambda x: x[0])\n",
    "        merged = [sorted_ranges[0]]\n",
    "        for current in sorted_ranges[1:]:\n",
    "            last = merged[-1]\n",
    "            if current[0] <= last[1]:\n",
    "                merged[-1] = (last[0], max(last[1], current[1]))\n",
    "            else:\n",
    "                merged.append(current)\n",
    "        return merged\n",
    "\n",
    "    merged_ranges = merge_ranges(all_ranges)\n",
    "\n",
    "    def assign_group(bmi_value, ranges):\n",
    "        for group_id, (start, end) in enumerate(ranges):\n",
    "            if start <= bmi_value <= end:\n",
    "                return group_id\n",
    "        return -1\n",
    "\n",
    "    noisy_df['final_group'] = noisy_df['bmi_final'].apply(lambda x: assign_group(x, merged_ranges))\n",
    "\n",
    "    # Step 3: 计算推荐检测时点\n",
    "    best_t_per_group = {}\n",
    "\n",
    "    for group_id in range(len(merged_ranges)):\n",
    "        group_mask = (noisy_df['final_group'] == group_id)\n",
    "        group_tstars = noisy_df[group_mask][target_col].values\n",
    "\n",
    "        if len(group_tstars) == 0:\n",
    "            best_t_per_group[group_id] = np.nan\n",
    "            continue\n",
    "\n",
    "        def risk_func(t):\n",
    "            total = 0\n",
    "            for t_star_i in group_tstars:\n",
    "                acc_risk = 10.0 if t < t_star_i else 0\n",
    "                delay_risk = 1.0 * max(0, t - 12)\n",
    "                total += acc_risk + delay_risk\n",
    "            return total / len(group_tstars)\n",
    "\n",
    "        res = minimize_scalar(risk_func, bounds=(11, 25), method='bounded')\n",
    "        best_t = res.x\n",
    "        best_t_per_group[group_id] = best_t\n",
    "\n",
    "    results['best_t_per_group'].append(best_t_per_group)\n",
    "    results['merged_ranges'].append(merged_ranges)\n",
    "\n",
    "    # Step 4: 汇总分组统计信息\n",
    "    group_summary = []\n",
    "    for group_id in range(len(merged_ranges)):\n",
    "        bmi_range = merged_ranges[group_id]\n",
    "        group_mask = (noisy_df['final_group'] == group_id)\n",
    "        group_data = noisy_df[group_mask]\n",
    "\n",
    "        bmi_median = group_data['bmi_final'].median() if not group_data.empty else np.nan\n",
    "        num_patients = len(group_data)\n",
    "        best_t = best_t_per_group.get(group_id, np.nan)\n",
    "\n",
    "        group_summary.append({\n",
    "            '组ID': group_id,\n",
    "            'BMI范围': bmi_range,\n",
    "            'BMI中值': bmi_median,\n",
    "            '孕妇数量': num_patients,\n",
    "            '推荐检测时点': best_t\n",
    "        })\n",
    "\n",
    "    results['group_summary'].append(group_summary)\n",
    "\n",
    "# Step 5: 结果分析与可视化\n",
    "all_best_t = []\n",
    "for sim in results['best_t_per_group']:\n",
    "    for group_id in sorted(sim.keys()):\n",
    "        all_best_t.append({\n",
    "            '组ID': group_id,\n",
    "            '推荐检测时点': sim[group_id]\n",
    "        })\n",
    "\n",
    "all_best_t_df = pd.DataFrame(all_best_t)\n",
    "\n",
    "# 绘制箱线图\n",
    "plt.figure(figsize=(12, 6))\n",
    "boxplot_data = [all_best_t_df[all_best_t_df['组ID'] == i]['推荐检测时点'].dropna().values for i in sorted(set(all_best_t_df['组ID']))]\n",
    "plt.boxplot(boxplot_data, labels=[f'Group {i}' for i in sorted(set(all_best_t_df['组ID']))], showfliers=False)\n",
    "plt.title('添加噪声后的推荐检测时点分布')\n",
    "plt.ylabel('推荐检测时点（周）')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# 计算置信区间\n",
    "confidence_intervals = []\n",
    "for group_id in sorted(set(all_best_t_df['组ID'])):\n",
    "    group_data = all_best_t_df[all_best_t_df['组ID'] == group_id]['推荐检测时点'].dropna().values\n",
    "    lower = np.percentile(group_data, 5)\n",
    "    upper = np.percentile(group_data, 95)\n",
    "    mean = np.mean(group_data)\n",
    "    confidence_intervals.append({\n",
    "        '组ID': group_id,\n",
    "        '均值': mean,\n",
    "        '5%分位数': lower,\n",
    "        '95%分位数': upper\n",
    "    })\n",
    "\n",
    "confidence_df = pd.DataFrame(confidence_intervals)\n",
    "print(\"\\n置信区间分析:\")\n",
    "print(confidence_df)"
   ],
   "id": "7fa317d824d7cf76",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\20900\\AppData\\Local\\Temp\\ipykernel_17884\\3067644774.py:156: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n",
      "  plt.boxplot(boxplot_data, labels=[f'Group {i}' for i in sorted(set(all_best_t_df['组ID']))], showfliers=False)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ],
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA98AAAIOCAYAAACh5r6OAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvc2/+5QAAAAlwSFlzAAAPYQAAD2EBqD+naQAAUolJREFUeJzt3XlcVPX+x/H3sKqAimCSSuaKqbnbZou75tqiZnVL0zJNbdHKsCxvuF7LtMyrlXuaWipq2m2h0iy1PbUSl2vKIiiCCijrzO8P7pyfEygDchgGXs/HgwfMmTPf85njB+o937NYbDabTQAAAAAAwDQeri4AAAAAAIDyjvANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AKFG5ubk6evSo4uPjlZCQkO8rJiZGZ8+ezfe6kydP6tixY8rNzXVB1QU7efKk3nnnHaWkpJTouLGxscrKyrrk86dPny5weVxcXKFjHzt2TG+88YbS09Mdls+fP1/R0dE6efKk3njjDZ0/f77Qsfbu3as1a9Y4LPvhhx+0fv36Ql+7efNm/fLLL/mWHzx4UN99952ys7MLHcNZmZmZOnPmTLFem5qaqhdffFEHDhxwav3t27fr4MGDxdrWxf/mWVlZ2rlzZ7HGmTp1qrZu3Vqs1wIAXMfL1QUAANxbTEyMpk+frhdeeEH16tXT2bNn1bdvX1WuXFmenp46c+aM/P395eWV95+cjIwMjR8/XkOHDnUYZ/PmzXr99df1888/q3Llyk5vPzk52Qg1FotFtWrVkiS9+eab2rVrV771a9eurddffz3fGOnp6fLy8pLFYjGWHzlyRK+//rrat2+vzMxMY3l2drays7N11VVXyd/f31hutVp1/vx5paenKy0tTadOnVJiYqJOnjyp2NhYHT9+XIcOHdKpU6f05JNPasyYMfnqy87O1h133KFJkybpgQcecHju/vvvV/fu3TV58uRL7o/Tp09r4cKFDmP/9ddfeuuttxQWFqagoCB99NFHSk1N1csvv+zw2pycHFmtVvn4+EiS9u/fr48++khDhgwx1vn555/17bff6t5775WU92FLVlZWvn+zd999V/369VObNm0cli9dulSbNm3Srl27VK1atXz15+bm6oknntDtt9+uBx98UGfOnNHJkyfzrdegQQOjpzZv3qx58+YVK8xmZ2fro48+Uq9evdS0adNC1w8PD9egQYPUpEmTIm3nxIkTGjhwoF577TXdfPPN+uOPPzRixAitWbMm3z66nMTERL3//vuqWbNmkbYPAHA9wjcA4IrUqFFD/v7+uuuuuzRr1iz16NFDv//+uyTp1KlTuvXWW7Vu3To1bNjwsuP4+PgoKCioSMFbkp588kn98MMPkqTq1atrz549kvLCToMGDTRo0CBj3U8//bTA2di1a9fq3//+t7y8vOTh8f8HhVmtVknSo48+6rDcHr7ffPNNdevWzVg+ZswYffnll7JYLKpcubICAgIUHBysmjVrqlatWurQoYP69++vkJAQhYSEFPh+jh49quzsbHXs2NFh+ZEjR5SYmKg777zzsvvD/mGAPUBL0rZt29SiRQt169ZNFotF06dPL/C133zzjUaNGpVveVhY2GWX1apVSzt27HB43tvbW1WqVHFYlpKSos8++0x169bV4sWLNX78+Hzjenp66uabb9aMGTP0+++/q3Xr1oqIiDDCZk5OjhITE7Vnzx5Vr17d2NbF73f+/Pl66623HMZt1KhRgbPF3t7ekmQE+cL4+voqNTVVR44ccVienZ0tDw+PS4byd955Rzk5OWrRooUkqXXr1urcubOmT5+uDz74oMDtp6WlydPTUz4+PvL09JQkrV69Wv7+/vrHP/6Rb32r1aqcnBxlZ2fLz8/PqfcDACg9hG8AwBXx8/PT7NmzNX/+fFWvXl379+9XpUqVZLFY9MMPP8jHx0c2m80hrGRnZ6tq1aqqXbu2EhISFBAQoNTUVAUEBDjMMHt7e2vMmDGKj4932ObIkSPVp08fY50pU6aocePGevrpp411cnJylJycrF9//dVYFhcXV+Dh3qNHj9bo0aPzLT948KD69eunzZs3q27duoXuC5vNpv79+2vWrFkOYd0ZmZmZSklJ0c8//6zQ0FD5+voqISFBlSpVUvXq1bV582YFBAQoLi7O4fDztm3bKjQ0VLm5uQ6H7FutVmVnZ8vLy0vr169Xbm6u7rvvPodtvvnmm8rIyNCwYcM0aNAgdezYUbt375a3t7csFos++ugjRUZGasaMGapbt64sFouWLl2q3bt3a9GiRQ7bsTt//rwRhK1Wq7G/fXx8tHDhQtWqVUvvvfeeBgwYoBtvvDHfhwySNGzYMF1//fW6cOGCkpKS1Lp1a61cuVJS3iH7Xbt2lbe3twYNGqRevXopODjY4fVeXl4KCQnR2rVrJUmLFi3Szz//rJSUFKWlpRkfsnh4eOjChQuSpLNnzyomJkY1atS4bHC1WCxav359viCflZWlRo0a6cMPP8z3mmPHjumjjz7Ss88+q4CAAGP5xIkTNWDAAC1YsEBPPvlkvtf179//kqcatG3b9pI1tmrVSuvWrbvk8wAA1yB8AwBKxNixYyUVPEtqD8oXGz58uCZOnKi+ffsqNTXVWN6yZUvj53feeUfHjx9Xu3bt1K5dO0l5gfHcuXPGOhfPGNpnMSVp0KBBio2Nddhmp06dHA4Tv9iZM2eUmprqMIb93OukpCRjO/aZxRo1auQ7bDo3N1eenp5OBe/c3FxlZmaqcuXKxgcVI0aMMJ6/4447JEl9+/bV7NmzFRkZqerVq2v58uXGOgcPHtTMmTMVGhqqH3/8UQ8//LDx3HXXXae2bdvq3nvvVWxsrB566CG1b99ekvTUU0/pqaeeUr169ZSdna3mzZtLygvIF88gd+3aVU2bNtXw4cPVvn17vfnmm+rXr59uvfXWSwbUXr16KTExUZL0+++/a9q0aXr++efVvHlzrVixQvPnz1ft2rX1wgsvaOzYsZo2bZp69+7tMIbNZjP+vTdt2iQpb+Z/zpw5euyxx4z1fH19Heq18/Lykqenp3F0gZ+fnzw9PbVs2TItWrTIIXzbPfvss8rNzdUbb7yhXr16FfyP9j+PPvqoxo0bd9l17KxWqyZOnKi6devmO42gfv36Gj9+vGbOnKmQkBANHjzY4fmlS5fKYrEYM9+zZ8/Wnj17tHr1asXFxenIkSMOR17YXdzDAICyg/ANACi2zMxMTZo0SQ899JBat24tSfrll19UqVIlJScnq2fPnoqIiFCbNm00aNAgRUZGKjg42GH2ecuWLfL19dXs2bMVHx+vN954Q5LUsWNH+fn5ycfHR61bt9aAAQMkScuXL3cIFxcHKIvFop07d2rChAnGsrCwMK1YscIIoZI0efJkvfDCC7r77ruN9TZt2qQ5c+bkO+87ICBAjz76qPHYHr4nT57scC60/bmNGzdq48aNTu/D7du3KyQkxAiR0dHRxnMvvPCCLBaLtm3bpnPnzikqKko1atQwnr/jjjvk6+srSWrTpo327NmjY8eOafDgwfrpp5909uxZI/Bdf/31DqHyhhtuMMK4XUZGhk6ePGnMfHt5eSkpKUmnT59Wz549lZCQIF9fX4WEhCghIcGY9Q4ICDDqWrp0qbKysnTPPffoiSeeUO/evXXu3DmNGzdOt9xyi5o3b66EhAR17NhR9957r5555hlt3rxZTz75pJo1a6ZvvvlG8+bN07x581SnTh2jNpvNpi+++MIhfFssFod/K7uCPvzw8PDQ008/rWeeecZheXp6utq2bavFixfrxhtvdHjOZrMZHyTY5ebmKi0tTQkJCQ7L/f39C/xg59///rf279+v1atXFxiKhw0bppiYGE2ePFlHjx7VM888Y/RCvXr1jPX27t2rLVu26K233lKdOnX09ddfa/Hixbr//vvzjQkAKJsI3wCAYsvKylJ2draGDBmiwYMH66WXXlKVKlWUm5urSZMmqWXLlsasZp8+fTRy5Ei9++67CgoKMsa4+uqrjbFq1qypGjVqKDU1VVarVdWqVXM6XNlZLBb5+/tr48aN+uCDD/TVV19JyguWEydO1D333KOHHnpINpvN4XX333+/evbs6fR7v/g92GVnZ+vee+/Vs88+W+jrc3NzlZGRYRwyfan3lJWVpdmzZ2v48OEOwVvKC/v2oGaftbZfnMzf319z586Vh4eH6tevr3PnzjkExpSUFMXHxysrK0vXXnutpLyZ6mHDhsnHx0cWi0U2m01paWmqVKmSXnnllXy12Q8rHzVqlHHkQ8OGDfX555/LarUqMDBQmZmZeuqpp1S9enUdPXrUmNGXpPvuu0/Tp0/XtGnTtHbtWv3zn/9UixYtVLVqVQ0aNEjvvfeesa79yANnzmW2WCyKi4tzOAqjZcuWslgsslqtio2N1TXXXFPoOOfPn3eo127ZsmVatmyZw7IJEyZo5MiRDss2btyot956S8OHD1dgYKBOnDhRYD8/9thj8vHx0dKlS/X1119r1qxZDkeAJCUl6amnntLAgQONmW4vLy9muAHAzRC+AQDFFhAQoDfffFMffvihvvzyS/n4+Oj8+fMKDw/XH3/8oddff12vvfaaxo0bp+eee05jxoxRr169NGLECPXp00ehoaHGWMnJybruuuskyTisvGrVqkWu6e/hxv74Usvt9u3bl++w4MvZsmVLvotrnTt3TldffXW+kHwlKlWqpJkzZyogIEBjxozR3LlzjdCVnZ1d4GHXdnfeeac6duyouXPnas6cOZo/f77xnH1W3Wq16ueff5YktWvXTvv27TPWefnll7Vv3z5NnTpVVatWLTDseXh46KqrrnJYZr8VWU5OjqpUqaKuXbtq9OjRqlq1qhHsLRaLsrKy5O3trU6dOhkfhgQGBmrRokWaMWOGatWqpUOHDjmMbZ/pvxyLxZLvnG/7RQDnzZunTZs26bPPPrvsvpPy9r0kzZgxQ/fcc88l1+vSpUu+fXPw4EG9+OKLGjJkiNLS0go8PPxio0aN0jvvvKP58+erfv36xvKzZ8/q8ccfV3x8vNatW5fvXO6/n+YxdepUh4sMAgDKDsI3AOCKDRo0SIMGDdK2bds0Z84cnT9/XsuXL9fZs2f17rvvaty4cfLy8tLChQu1dOlSLVy4UN9++62WL19uzPgeO3bMOCzafg54QbeiKozNZlNGRoZxVXB7QPbz89Prr79u3Gbs7+HbHupWrFhhHH5sv+BaVFSUccG1I0eOqHfv3gUGtzNnzqhGjRr673//W+hVydu0aZPvHtpS/jA1cOBA3XzzzUpJSdF3332nyMhII1xlZ2fnC6P2+10PGTJEbdq00cSJEzV37lxNmTLFOHQ/LCxMixYtynfY+cWmTZum3bt3a+3atRo6dKiOHTvmEDAvXLignJwc1ahRw+GWbn/99Zd27typmjVrauHChfrPf/6jDz74QD/99JPat2+vl19+WT4+PnrppZfk7e2t7t27a8SIEQ6HT3t7eys8PFxeXl5GKE9OTlaVKlWMfvn7kQsXs9ls+c75thsyZIiWLFmiyMjIfOdY/52np2eBM9UF+fvVyps0aaJ169apefPmSk9P14QJE/TLL7/o8ccf13/+8x+HDyw6duyounXr6vbbb9ftt99uLE9MTNTIkSN17tw5tWjRQs2bN9cTTzwhSfr444+1du1a40J0Ul6vMBsOAGUX4RsAUCK+//57XXPNNWrUqJGeffZZNWzYUL/99pukvBCTlpamjIwM5ebmKioqSunp6UaQOnv2rOLj442Z79TUVPn4+Bgzj4XZtm2bcdupm2++Wdu3b3e4QNodd9yh999/32Gm/eIrdBfH30NZVlaWTp8+rdDQUCOYf/jhh8Zh9RdbsmSJ9u/fX+C427dvN36eMWOG8XNgYKD69++v9957TwMHDpTFYnGY+d6yZYsWLFhgnNf+6KOPOgS5rKwspaenG48zMzOVlpamrKwsh5n6nJwczZw5UytXrlTPnj0VGBiozZs3G8+np6cbRzs8/vjj+a6g/tZbb6lz586Ki4tT586dtWjRIs2dO1cLFy5UZGSkQkNDtWzZMk2aNEkHDx5UTEyMw7nNdpGRkfrPf/6jHj16SMoLovZ7uNvfz6VcfNV3O3tYv/rqq9W9e3ctWrTosrPZ9n1hs9nyHbJf0PYK2qb9tmL2c8FjY2NVtWpVh5nt1NRUXbhwIV+fxMXFadCgQapUqZKWL1+umTNnys/Pz/hAoVq1ag4fMEh5RyEQvgGg7CJ8AwCuWEJCgoYNG6b3339fCxcu1LBhwxQUFKSHHnpIUt59tBcvXqzly5dr3rx5qlevnhGqJGnPnj2qXLmyw2HnRTnkvHfv3mrcuLEmTJigH3/8UY888ogxS3rhwgVlZmbqzjvvlL+/vzw9PZWTkyOLxaLdu3cbYyQlJUmSwxXD7bp27Zpvmf0e4HYHDhxQTk6OwsLCjOdq1Khh3J/6YlWqVDHu2/x3F4epv9/zfPDgwVqzZo327t2rFi1aKDs721indevW6tevn2666Sbdf//9+Q5zfumll/TSSy8Zj4cPHy4p71xx+6Hmhw8f1uTJk5WYmKju3btLyrvi+/nz541QFxUVpeXLl+v9999X3bp1lZWVpaNHj6p+/fr65ptv9PHHH+vDDz/UlClTVKdOHW3cuFGNGzfWl19+qQ8++EBPP/205syZoz179ui3337Ttddeq1tuuSXffli1apUaNGigxo0b684779SPP/6o2rVrq1q1anrqqafk6+ur3NzcAj9EsVqtys3NNQJzenq6w7/X0KFDlZKSIi8vL4db2/2d/bkZM2Y4fBBSkMt9GGAXFRWlG264wWGZvca/h+86dero+eef18033+zwoQMAwH0RvgEAV2zx4sVq1KiR2rRpo8TERH3//ffasGGDMjIyJEkdOnTQq6++qsTERPXr10+LFy92CN9btmzRrbfeagS81NRUp8N3Tk6O8XNmZqZuuOEG4/ze8PBwpaWlKSoqSg0aNFCXLl0c7gV+sfj4eNWsWVOffvrpZbf3119/6Z577nHYrpT3AUK9evVUq1aty86SXonmzZtr7dq1atWqldLS0iT9/6xqaGionnjiCR08eLDA1/7rX/9yOOx81apVatOmjUNonD17tlJTU7Vy5UotW7ZMiYmJWrx4sd5//31jhj03N1c2m02jRo2SJONe4j/99JPatGmjcePGOVwsrHHjxpLyPjiYO3euwsPD1aJFC+3bt0+fffZZgecn//zzzzpw4IBmzZqlsLAwtW7dWt26ddOQIUP0ySefGIde+/r6FnihOqvVqoSEBIeLpdk/2JHy7oPtjNjYWO3du7fA88ytVqvDtgsL37t27dJ3332nhQsXOiy3X0394g9d7O666y7j58sdZn9xTQCAsqvwG5ECAHAZx44d0wcffKBx48bJYrFo9erVuvHGG9W0aVNjnQYNGigsLEyffvqpHnroIf3666/au3evpLzZ1qioKD344IPG+mfOnHE43zs8PFxhYWEKCwvT77//7nCIrz30tGzZ0rgntJQ3yxgVFaXw8HBJ0nPPPadVq1bp22+/LfB97NixQ23btpWfn99lv+rVq6d//etf+S4ytmHDBmOG3JmgdKl1EhISjK8LFy7ke95+Szf7ud3OXP37Ujw8POTp6WlsZ/bs2VqzZo3DLb6ef/557d27Vz/++KN+/PFHLVy4UJ6ensbj3377TT/99JOkvA8C7Fc9/7vu3bsrODhY8fHxev/999W1a1cdPHiwwEO/33rrLXXp0sU4/33z5s1KSEhQgwYN9Oqrr2rJkiWS8m5rNnTo0Hyvz83NVZ06dRQdHa3o6GhNmDChyBfBy8rK0sMPP6yZM2dKyjsH3n4huZSUFPXu3ds4deD48ePq2rWrDhw4UOBYP/74o55++ml17txZnTt3dnjur7/+UtWqVQv9d7xcuN+4caMiIiKUlJRUohf7AwCULGa+AQDFlpubq5dffllhYWHq3r274uLitGLFCs2ZM0eSY8C89dZb9dlnn+nFF19Uu3btFB0drWbNmumf//ynrrvuOt1000366aef9Pnnn+uzzz5Tu3btJOXNrI4fP944jPqpp55ymHW+99571bhxY/n4+BiH53744YeaNm2a3nzzTdWuXVs2m03169fXlClT9MQTT+jRRx/V0KFDjdn1X3/9VV9//bUWLVpU6Hv29/c3ZpDtlixZYsyIS/8/A1nQ4ep2bdu2dXhsf83fb211qfOSDx48KG9vb4fQduLECf3111/51rXZbHr++ef1/PPPG8su/rCjcePG+vjjj4t1dfnCZGZmKiMjQwEBAVq/fr0sFotycnK0atUqde7cWb6+vkpJSZGPj4/8/Py0bds2fffdd1q1apWkvKA7b948DRo0SN26dVN4eLhmzJihW265xfiA5++HntesWdPhSvQjR47Mdxuw3NxcWa1WxcXFScp/q7ctW7YoNTVVjzzyiPE+lixZonvvvVeBgYEKCgrSrFmztHLlSoWGhqp27dqaPHmy1q5d63AtA/upGG3btjV+LyRp69atio6O1oYNG5yaibffd93OarUav1+nTp3S1q1bNWjQoMteRA8A4FqEbwBAsR04cECHDx82ZgdDQkK0fPlytWjRQmvWrFFkZKT8/Pzk6empf/zjHxoyZIgkaeXKlfL09NS2bdv0yy+/GFdsrlKlir7++mu1aNHCmEG13/+7YcOGkqRatWo5hJCLD81NS0vTE088od9++01z5szR7bffLqvVKqvVquzsbPXp00eVK1dWeHi41q1bp82bNys9PV1PP/202rVrV+A9nZ1x0003acaMGcYh1vYwuHXr1gIvuLZo0SLj/uN29pnN6OhoY9kLL7zgMOOZnJys2bNnKy0tTd9995169erlcOG3kSNH6uDBgw4XlpPygtrUqVONe67b2Wy2S86oZmdnO4TaxMRE/f7779qzZ0+ht+iybzMnJ0dfffWVnnrqqUuu99lnn0mSxo4dq3HjxiklJUWtWrVS+/btlZWVpbFjxyonJ8cY44EHHtCWLVu0bds2NW3aVC+//LK++OILh9n6wYMHF3ol89zcXA0YMED//e9/FRwcrEaNGhnPXbhwQW+//bb69u1r3A984MCBWrt2rXbt2qWbb75ZTz75pB5++GHj8eTJk3XvvfdqzZo1xi3r5s2bpw0bNmjs2LEaMWKEQ99aLBZt2LBBN910k5577rlC9+fF9zy312//EOqxxx7L9+ECAKDssdicOTYOAIBLOHPmjHGl8Yt9//33Wr16te677z7dfPPNl3z9iRMnCgyoxbVr1y75+fkZ5x1nZGSoVatWDvflTk5O1uHDh3XDDTfowoULevPNNzVkyJACr7pdHNnZ2UpMTFRISEi+W1BdSmJionbv3u0wq/7XX38pNzfX+OBBknEY/S233KI+ffo4zNi+/PLLSkpK0pgxY9S8eXNjec+ePfXEE0/km7G/nEmTJhnnfEt5gfT2229XlSpVdP/99xvnfF/KnXfeqQcffFBDhgyRzWaTl5fXJW/blZOTI6vVaoT6zMxM+fr6ymaz6ZNPPlGdOnUcZoeTk5ONw6sjIyMVGxuru+++2yGAO+Onn35SVlaWWrVqpSpVqhjLs7KytHnzZnXo0MGhJyIjI3XnnXca54Bv3bpV3bt3N+peuXKlunfvbpy/nZubq6SkJFMumLZs2TK999572rlzZ4mPDQAwB+EbAAA4JScnx+kPEwAAgCPCNwAAAAAAJuNq5wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJisXN4v5PTpVHEN99JhsUhBQQHsc5Rr9DkqAvocFQF9joqAPi999n1emHIZvm020WiljH2OioA+R0VAn6MioM9REdDnZQ+HnQMAAAAAYDLCNwAAAAAAJiN8AwAAAABgMsI3AAAAAAAmI3wDAAAAAGAywjcAAAAAACYjfAMAAAAAYDLCNwAAAAAAJiN8AwAAAABgMsI3AAAAAAAmI3wDAAAAAGAywjcAAAAAACYjfAMAAAAAYDLCNwAAAAAAJiN8AwAAAABgMsI3AAAAAAAmI3wDAAAAAGAyL1cXAAAAnPPXX0d17txZE7eQJcnHlJGrVq2ma6+tb8rYAAC4A8I3AABu4PTp07rppjayWq2uLqVYPD09tX//YQUFBbm6FAAAXILwDQCAGwgKCtLu3b+YNvN96FC0Ro9+TP/+97tq3DisxMevWrUawRsAUKERvgEAcBOlcdh248ZhatmytenbAQCgouGCawAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJjMZeE7JSVFXbp0UWxsrLFs/fr16tu3r9q3b6/x48crOTnZVeUBAAAAAFBiXBK+k5OTNWrUKMXFxRnLvvvuO02dOlXh4eHatGmT0tLSNHbsWFeUBwAAAABAiXJJ+B4/frx69+7tsCwyMlKDBg1Sx44dVadOHT3//PP66aeflJKS4ooSAQAAAAAoMS4J3xERERo6dKjDspSUFNWuXdt47OGRV5qXl1ep1gYAAAAAQElzSbINDQ3Nt+y6665TVFSUhg4dKovFog0bNqhly5YKCAgo8vgWS0lUCWfY9zX7HOUZfY6K4OI+p9dRXvH3HBUBfV76nN3XZWZaefjw4Ro1apTuuece+fr66pdfftGsWbOKNVZQUNEDO64M+xwVAX2O8qx6dT/je3AwvY7yjb/nqAjo87KnzITv6tWra82aNTp27JgWL16ss2fPql+/fsUa6/TpVNlsJVwgCmSx5P1is89RntHnqAjOnEk3viclpbq4GsAc/D1HRUCflz77Pi9MmQnfdldddZU+//xzRUREyNPTs1hj2Gyi0UoZ+xwVAX2O8sze2/Q5KgL6HBUBfV72uOw+35eycuVKNWjQQN26dXN1KQAAAAAAlIgyNfN97tw5LV68WO+++66rSwEAAAAAoMS4NHxHR0c7PK5atar27NnjomoAAAAAADBHmTvsHAAAAACA8obwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmMxl4TslJUVdunRRbGyssSwyMlKdOnVSmzZtNGzYMIfnAAAAAABwVy4J38nJyRo1apTi4uKMZcePH9fcuXP19ttva+vWrapdu7bCw8NdUR4AAAAAACXKJeF7/Pjx6t27t8OyP/74Q61atVLz5s1Vu3Zt3XPPPTp69KgrygMAAAAAoES5JHxHRERo6NChDssaNWqk3bt3648//lBqaqpWr16tjh07uqI8AAAAAABKlJcrNhoaGppvWaNGjdSzZ0/dfffdkqS6devqww8/LNb4FssVlYcisO9r9jnKM/ocFcHFfU6vo7zi7zkqAvq89Dm7r10Svgvy66+/6quvvtKHH36ohg0b6p133tFjjz2mjz76SJYidk5QUIBJVeJS2OeoCOhzlGfVq/sZ34OD6XWUb/w9R0VAn5c9ZSZ8b9u2TX369FHLli0lSU8//bTWrFmjAwcO6LrrrivSWKdPp8pmM6NK/J3FkveLzT5HeUafoyI4cybd+J6UlOriagBz8PccFQF9Xvrs+7wwZSZ85+bmKjk52Xicnp6u8+fPKzc3t8hj2Wyi0UoZ+xwVAX2O8sze2/Q5KgL6HBUBfV72lJnw3bZtW02aNEnLli1TUFCQPvzwQwUHByssLMzVpQEAAAAAcEXKTPju3bu3jh49quXLl+vUqVNq3Lix3nrrLXl7e7u6NAAAAAAArohLw3d0dLTxs8Vi0dixYzV27FgXVgQAAAAAQMlzyX2+AQAAAACoSAjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyVwWvlNSUtSlSxfFxsZKkjZs2KCwsLB8Xxs2bHBViQAAAAAAlAgvV2w0OTlZo0ePVlxcnLGsb9++6tatm/H4/Pnzuuuuu9ShQwdXlAgAAAAAQIlxycz3+PHj1bt3b4dlPj4+qlq1qvEVGRmpHj16KDQ01BUlAgAAAABQYlwSviMiIjR06NBLPp+ZmakVK1Zo5MiRpVgVAAAAAADmcMlh54XNZm/ZskWtWrVS3bp1izW+xVKsl6EY7PuafY7yjD5HURw5clhpaWmuLqPIDh2KdvjuTvz9/dWwYSNXlwE3wN9zVAT0eelzdl9bbDabzdxSLi0sLExRUVH5QvbAgQM1btw43XHHHS6qDACAojt06JCaNGni6jIqpIMHD6px48auLgMAgEtyycz35Rw7dkzHjx/XLbfcUuwxTp9Oles+UqhYLBYpKCiAfY5yjT6Hs44fT5Ak/fvf76px4zAXV1M0Fotks2XJYvFxqz4/dChao0c/puPHExQYGOLqclDG8fccFQF9Xvrs+7wwZS58f/LJJ+rUqZO8vb2LPYbNJhqtlLHPURHQ53BW48ZhatmytavLKBKLRQoODlBSkvv+z5q71o3Sx99zVAT0ednjsvt8X8o333yjG2+80dVlAAAAAABQYspU+M7IyNBvv/2m1q1bu7oUAAAAAABKjEsPO4+OdryiaqVKlbR//34XVQMAAAAAgDnK1Mw3AAAAAADlEeEbAAAAAACTEb4BAAAAADAZ4RsAAAAAAJMRvgEAAAAAMBnhGwAAAAAAkxG+AQAAAAAwGeEbAAAAAACTEb4BAAAAADAZ4RsAAAAAAJMRvgEAAAAAMBnhGwAAAAAAkxG+AQAAAAAwmVdRX5CRkaE9e/YoLi5OAQEBatiwoZo1a2ZGbQAAAAAAlAtOz3zbbDYtWLBA/fr105dffqn09HQdPXpUr7/+uvr27atdu3aZWScAAAAAAG7LqZnvjIwMjRo1SmFhYdq6dat8fHwcno+Ojtazzz6ru+66SyNGjDClUAAAAAAA3JVTM99jxoxRp06dFB4eni94S1JYWJg++OADbd26VV9++WWJFwkAAAAAgDtzauY7PDxcjRo1uuw6/v7+evfdd+Xn51cihQEAAAAAUF44Fb4LC952QUFBV1QMAAAAAADlEbcaAwAAAADAZE7NfG/fvl0+Pj7y8rr86p6enmratKmqVKlSIsUBAAAAAFAeOBW+x40bp2bNmslmsxnLLBaLw2NJSklJUc2aNbVq1aqSrRIAAAAAADfmVPgOCQnRmjVrjMdZWVmaPn26pkyZ4rBeQkKC7rzzzhItEAAAAAAAd+fUOd8Wi0WSlJ2drcGDB8vT01Mff/yxJOn5559XeHi4nnvuOVWuXFmdOnXKNyMOAAAAAEBF5lT4todpb29vHTt2TJ6ensb53zt37lSrVq30zTffqFq1anrjjTeMsA4AAAAAAIpwtfOlS5cqPDxcGRkZmjx5shGwAwICNGTIEC6yBgAAAADAJTgdvhs1aqR27drJw8NDbdu2zfc8s90AAAAAABTM6fB92223acCAAbJYLOrfv7+sVqsk6dSpU5owYYKSk5NNKxIAAAAAAHfm1NXO7bPaFotFd999t3JycpSZmSkp74Jrvr6+at++vXlVAgAAAADgxpwK32lpadq0aZMsFovatGmjqKgoPfnkk9q1a5c6dOig0NBQ+fj4mF0rAAAAAABuyanw3apVK3399dfy9PSUh4eHrFarLly4oKioKB07dkxnzpxRkyZN1KVLFw0cOFAhISFm1w0AAAAAgNtwKnwvWLDgss8nJSXpyy+/1KpVq/Tdd99p9erVJVJcRfJ91Eadif3TlLFjYmKUlpZqytiS5OPjpaysHFPG9vcPUGhoqCljV697nW7oercpYwMAAADAxZwK34UJDg7W4MGDde+99yo11byQV14dOnRQPywcqSmdfM3ZQOD/vtxVmjnDTlmYqcBrmqtx4ybmbAAAAAAA/qdEwredp6enqlevXpJDVgiNGzdRyqh3tIqZ73zMnPnuMOo6gjcAAACAUuFU+F67dq3uueceeXt7X3a93377TdWrV1e9evVKpLiKJO/wZ/c7BNpikYKDA5SUlCqbzdXVAAAAAEDZ5NR9vuPj4zVx4kTl5uZecp2DBw9q/PjxSk9PL7HiAAAAAAAoD5wK388884zq16+vf/zjH/rzT8dDo7OysrRq1SqNGjVKU6dOVbNmzUwpFAAAAAAAd+X0Od/jxo3TzTffrNdee02JiYmqU6eO0tPTlZCQoNtuu01r165VzZo1zawVAAAAAAC3VKQLrrVv316LFy9WRkaG4uPj5efnp+DgYHl6eppVHwAAAAAAbq9YVzuvVKmSGjRoUNK1AAAAAABQLjl1zjcAAAAAACg+wjcAAAAAACYjfAMAAAAAYDKXhe+UlBR16dJFsbGx+Z577bXXNGrUKBdUBQAAAABAySvWBdeuVHJyskaPHq24uLh8zx08eFCrV69WZGRk6RcGAAAAAIAJXDLzPX78ePXu3TvfcpvNppdffllDhw7VNddc44LKAAAAAAAoeS6Z+Y6IiFBoaKimT5/usHzdunU6cOCABg0apK+++kq33nqrvL29izy+xVJSlaIw9n3NPkd5Rp+jONytX8pDn7tz7Sgd5aHPgcLQ56XP2X1douE7OTlZ8fHxatGixWXXCw0NzbcsPT1dc+fOVb169ZSQkKBNmzZp4cKFWrFihXx9fYtUR1BQQJHWx5Vjn6MioM9RmMBAP+N7cLB79ou79Xl52Ocofe7W50Bx0Odlj1Ph22q1aujQoVq5cuUl11m3bp1ee+01de3aVTNmzChyIZ9//rkuXLig5cuXq3r16nr88cfVr18/RUZG6r777ivSWKdPp8pmK3IJKAaLJe8Xm32O8ow+h7NSUtKN70lJqS6upmjctc/deZ+j9LlrnwNFQZ+XPvs+L4xT4dvDw0P79+83Hk+YMEFeXl6yWCzKzs7WpEmT9MEHH+hf//qXOnXqVKyCExIS1LJlS1WvXj2vMC8vhYWFFXg19MLYbKLRShn7HBUBfY6icNdecec+d9e6Ufrcuc8BZ9HnZY/TF1y7+NDvL7/8Uk2bNlXjxo315ZdfKigoSBs3bix28JakkJAQZWZmOiyLj49X7dq1iz0mAAAAAABlgdPh23LRWeS+vr565JFHNGLECCOUJyYmKjw8XPPnzy9WIZ06ddKRI0f0wQcfKCEhQStWrNCff/6p2267rVjjAQAAAABQVhTrVmMXB3GLxaJz586pX79+slqt6tWrV7EKqV69ut577z1t2rRJPXv21PLly/XGG2+obt26xRoPAAAAAICywqlzvl977TVduHBB8+bNK/D5qlWr6j//+Y9q1KhRpI1HR0c7PG7durXWrFlTpDEAAAAAACjrnArf8fHxuvPOO5WQkHDJdQIDA/XFF1/Iz89PN998c4kVCAAAAACAu3MqfM+ZM8fh8U033eTwODc3V71795bVatWjjz5K+AYAAAAA4CJOhe+/s9lsOnHihKxWq6xWqzw9PbVgwQI1bNiwpOsDAAAAAMDtFSt8nz17Vl26dJHNZpPFYlFiYqL++OMPNWjQwOFibAAAAAAAoJhXO//xxx/122+/ad++ffrxxx91/PhxzZgxQz179tSWLVtKukYAAAAAANxasWa+/f39jZ+9vb3VoUMHbd++XevXr9fx48dLrDgAAAAAAMqDYoXvgnh7e2vIkCElNRwAAAAAAOVGkQ47z8jIUN++fc2qBQAAAACAcsmpme9Dhw7J09PTuLhaTEzMJdcNCQmRt7d3iRUIAAAAAIC7cyp89+/fX56envLy8lJmZqb69+9f4HrZ2dmqU6eOPv300xItEgAAAAAAd+ZU+P7zzz+Nn2+88Ubt2bOnwPWysrJ044036tixY6pXr17JVAgAAAAAgJsr1q3G7HJzcx2CuI+Pj5577jldddVVV1wYAAAAAADlRZGudm61Wo2fjx07pqeeekqBgYG68cYbjeUPPPBAyVUHAAAAAEA5UKSZ75ycHGVlZUmSbDabevXqpSVLlphSGAAAAAAA5UWRZr4tFoseffRRSdK1116rUaNGmVIUAAAAAADlidPhu1evXkpPT5enp6fWr18vi8UiKW8G3Gq1Kjs7Wxs2bFCtWrVMKxYAAAAAAHfkdPg+c+aM3nvvPUl5gXv48OFaunSpw881atQwrVAAAAAAANyV0+HbYrGoRYsW//9CLy/j8cU/AwAAAAAAR06Hb5vNpueff954nJ6ebjy2/1ytWjW9+OKLJV8lAAAAAABuzOnwPXLkSOXk5MjLy0sWi0XNmzeXzWZTbm6uGjVqpNzcXFWuXNnMWgEAAAAAcEtOh+/hw4ebWQcAAAAAAOWWU+F73bp18vHxcWrAhg0b6vrrr7+iogAAAAAAKE8KDd82m03bt2+Xj4+PcXuxS8nMzNQrr7yinTt3KiAgoMSKBAAAAADAnRUavi0Wi95++20lJiZq165d8vT0lM1mM563Wq3KyclRr1695O/vr0ceeUTJycmEbwAAAAAA/sfpc75TUlL03XffydPT02G5/aJrd9xxh/z9/bV06dISLxIAAAAAAHfmVPg+deqUDhw4oNtuu63AWe8BAwbI29tbixcvVkxMjKZMmWJWvQAAAAAAuB2nwvfRo0f173//W23bti3w+a5duyo8PFwxMTGaOnVqiRYIAAAAAIC783B2xQ4dOujpp59WSkqKcb/v4OBgNW/eXAcOHFCjRo300UcfqU2bNmbWCwAAAACA23H6nG+LxSIvLy917dpVvr6+stlsSklJ0X//+18tX75cOTk5qlevngYOHFjoVdEBAAAAAKhInA7f27dvV1xcnMM531JeKF+zZo327t2r1157Tdu2bdObb77J1c4BAAAAAPgfp8J3s2bNNG3aNNWsWdNhudVq1eHDh1W1alV17txZHTp00PTp0+Xt7W1KsQAAAAAAuCOnzvk+efKknn/+edWoUUNNmzbVgQMH9NtvvykoKEivvvqqHn30Uf3www/y9/fX9OnTValSJbPrBgAAAADAbTgVvl999VU98MADuuqqq/Tzzz9r+vTpatiwoWrVqqWvv/5anTt31sSJEzV48GB98cUXZtcMAAAAAIBbceqw81mzZumqq67Ke4GXl6ZPn6727dtLkvz9/TVs2DDdf//9mj9/vtLT082rFgAAAAAAN+RU+K5Vq5bxc8uWLQtcx9fXVxMmTCiZqgAAAAAAKEecvs+33alTpzRkyBAlJibmey4rK0uvvPJKiRQGAAAAAEB5UeTwvWjRImVmZiowMFB9+vTRG2+8odjYWEmSp6endu/eXeJFAgAAAADgzooUvr/77jtt2bJFAwcOVHZ2toYOHar4+HgNHDhQI0aM0KeffmpWnQAAAAAAuC2nzvm2Wq1av369li5dquXLl+uhhx7Sgw8+qMGDB+uuu+7SF198oezsbG3bts3segEAAAAAcDtOhe9evXqpUqVKWrdunfz9/WWxWHT+/HlNnjxZ27dvV61atTRjxgzNnz9fPXr0MLtmAAAAAADcilOHnY8ZM0YBAQEaNmyYDh8+LKvVqipVqigsLEwfffSRlixZosOHD5tdKwAAAAAAbsmpme8BAwZowIAB2rhxox588EE1adJEVqtVrVu31rRp0/T999+rbdu26tKliywWi9k1AwAAAADgVpwK33Y9evRQUFCQnnrqKa1fv17169dX06ZNNWPGDAUHB8tqtcpqtZpVKwAAAAAAbqlIVzuPjIzUvn37NHfuXIWFhWnp0qU6cuSIgoODJUm5ubl66KGHnBorJSVFXbp0MW5TJkkREREKCwszvrp3716U8gAAAAAAKJOcmvl+7733FBAQoF9++UWZmZkKDg7W/v379eeff+rhhx/W2rVrJeWFb2dmvpOTkzV69GjFxcU5LP/999/1zjvvqE2bNpIkD48i34YcAAAAAIAyx6nwffz4cVWqVElJSUmKi4vTnj17dO7cOYWFhenEiROy2WyS8m5JlpmZWeh448ePV+/evfXrr78ay3JycnTw4EG1b99efn5+xXs3AAAAAACUQU6F71dffVWStHr1ap07d06PPvqooqKitHTpUu3atUuPPfaY+vbt6/TF1iIiIhQaGqrp06cby6Kjo2Wz2XTXXXcpMTFRHTp0UEREhGrXrl3kN8U130qPfV+zz1Ge0ecoDnfrl/LQ5+5cO0pHeehzoDD0eelzdl8X6YJrHTt2VHZ2try8vNSzZ0/17NlTn376qb7//nv16dPH6fAdGhqab9mRI0fUuHFjvfTSSwoMDNS0adP08ssv67333itKiZKkoKCAIr8GV4Z9joqAPkdhAgP9jO/Bwe7ZL+7W5+Vhn6P0uVufA8VBn5c9RQrf9erVy7fMHsKvVP/+/dW/f3/j8eTJk9WtWzelpaXJ39+/SGOdPp2q/x0JD5NZLHm/2OxzlGf0OZyVkpJufE9KSnVxNUXjrn3uzvscpc9d+xwoCvq89Nn3eWGcDt9DhgzRmjVrlJOToxUrVmj48OGy2WyyWCzq16+ftmzZckUF/13VqlVltVp18uTJIodvm000Wiljn6MioM9RFO7aK+7c5+5aN0qfO/c54Cz6vOxx+nLiCQkJxs/Lli2TJLVq1UoZGRk6e/bsFRcyY8YMbdu2zXi8b98+eXh46Oqrr77isQEAAAAAcCWnZ749PT3zXuDlJW9vb0lSVlaWPv/8c1WuXPmKC7nuuus0d+5c1axZUzk5OYqIiNDdd99dImMDAAAAAOBKToXvDRs26MKFC9q0aZMGDBhgLA8KCtKKFSuUkpKiqVOnSpJuu+023XHHHUUu5K677tKRI0f0xBNPyM/PT926ddP48eOLPA4AAAAAAGWNU4edf/vtt7pw4YJ27drlsNzX11cjRoyQxWJR7dq1Vb16dUVERDi98ejoaNWtW9d4PGHCBP3www/6+uuv9dJLL6lKlSpOjwUAAAAAQFnlVPh+/fXXFRgYqJkzZzost1gs6tWrl6pVq6bhw4dr7NixyszMNKVQAAAAAADcVZFuNbZv3z69//77kqScnBxlZWVJkmwXXUbvs88+K8HyAAAAAABwf07NfO/Zs0fZ2dn65ZdflJSUpNOnT+ubb77RuHHjJEkpKSnGulwgDQAAAAAAR06F7zfeeENnz57V559/rsWLF8vLy0svv/yyEhMTFRUVpVdeeUVRUVH64osvHG4XBgAAAAAAnDzsfM2aNerSpYtWrlwpSapWrZoWLVqkYcOGScq7TZjNZpPValVmZqZ69+5tWsEAAAAAALibIp3zbWez2dSoUSMtX75cDz74oB555BHdcsstJV0bAAAAAADlglOHnUtSenq6JCk7O1uenp6SpIYNG+qll17S+PHjlZqaak6FAAAAAAC4Oadnvvfs2aNz586pcuXK+vzzz43lffv2VZUqVRQQEKC1a9fqvvvuM6VQAAAAAADcldMz35L0zDPPaPv27crKytKUKVMkSVOnTtVtt90mSZozZ06JFwgAAAAAgLsrUviuXLmyvL295eXlpZ07d0qStm/fLm9vb0mSv79/yVcIAAAAAICbK/Swc5vNptWrVys+Pl4eHh7avXu3UlJSdOHCBUVGRur8+fOKjIw0rnYOAAAAAAAcFTrznZ2drW3btqlr166SpP3792vHjh3KyMjQjh07dOHCBe3YsUPffPONsrOzTS8YAAAAAAB3U2j49vHx0aJFi9S2bVtJ0uOPP645c+YoODjY4fucOXPk5+dnesEAAAAAALibQg87z8zMVNeuXdWjRw+lpKQoKSlJMTExys7OVkxMjHJychQTEyNJxrLQ0FDTCwcAAAAAwF0UGr7tM9+ffvqpDh48qPDwcFWtWlU1atTQyJEj5evrq5EjR0qSvL29NWzYMEVFRZleOAAAAAAA7qLQ8G2xWNS6dWu1bt1aY8aM0aJFi7Rq1SqNHDlSd999d2nUCAAAAACAWyvSrcZ8fX31ySefaNWqVWrUqJEWLFhgVl0AAAAAAJQbhc58S9K0adNUtWpVWSwWnT171jis/JNPPnG4vZjFYlH37t3VpEkTc6oFAAAAAMANOTXz7eXlJW9vb/n4+MjDw0O+vr7y8fFRUFCQUlNT5ePjIx8fH504cUIRERFm1wwAAAAAgFtxauZ74sSJkqScnBytWrVKjz32mCTpqquu0uHDh40Lrh06dEizZ882qVQAAAAAANyTU+HbzsPDw+E87w4dOqh58+bG48aNG+udd94pueoAAAAAACgHihy+Lw7bderUKfGCAAAAAAAob4p0tXMAAAAAAFB0hG8AAAAAAExG+AYAAAAAwGSEbwAAAAAATEb4BgAAAADAZIRvAAAAAABMRvgGAAAAAMBkhG8AAAAAAExG+AYAAAAAwGSEbwAAAAAATEb4BgAAAADAZIRvAAAAAABMRvgGAAAAAMBkhG8AAAAAAExG+AYAAAAAwGSEbwAAAAAATEb4BgAAAADAZIRvAAAAAABMRvgGAAAAAMBkhG8AAAAAAEzmsvCdkpKiLl26KDY2tsDnR4wYoQ0bNpRyVQAAAAAAlDyXhO/k5GSNGjVKcXFxBT6/efNm7dy5s5SrAgAAAADAHC4J3+PHj1fv3r0LfO7MmTOaNWuW6tevX8pVAQAAAABgDpeE74iICA0dOrTA52bNmqVu3bqpdevWpVsUAAAAAAAm8XLFRkNDQwtcvnv3bu3atUsff/yxpk6dWuzxLZZivxRFZN/X7HOUZ/Q5isPd+qU89Lk7147SUR76HCgMfV76nN3XLgnfBcnMzNQrr7yiKVOmyN/f/4rGCgoKKKGq4Cz2OSoC+hyFCQz0M74HB7tnv7hbn5eHfY7S5259DhQHfV72lJnwvWDBArVo0UKdOnW64rFOn06VzXblNaFwFkveLzb7HOUZfQ5npaSkG9+TklJdXE3RuGufu/M+R+lz1z4HioI+L332fV6YMhO+t2zZopSUFLVv316SlJGRoU8++UR79+7VlClTijSWzSYarZSxz1ER0OcoCnftFXfuc3etG6XPnfsccBZ9XvaUmfC9evVq5eTkGI//9a9/qVWrVrr77rtdWBUAAAAAAFeuzITvkJAQh8dVqlRRYGCgatSo4aKKAAAAAAAoGS4N39HR0Zd8bubMmaVYCQAAAAAA5nHJfb4BAAAAAKhIysxh5wDKv++jNupM7J+mjB0TE6O0NPOudOzj46WsrJzCVywGf/8AhYaGmjJ29brX6YauXDsDAADA1QjfAErFoUMH9cPCkZrSydecDQT+78tdpZkz7JSFmQq8prkaN25izgYAAADgFMI3gFLRuHETpYx6R6uY+c7HzJnvDqOuI3gDAACUAYRvAKUm7/Bn9zsE2mKRgoMDlJSUyv0yAQAAUCxccA0AAAAAAJMRvgEAAAAAMBnhGwAAAAAAkxG+AQAAAAAwGeEbAAAAAACTEb4BAAAAADAZ4RsAAAAAAJMRvgEAAAAAMBnhGwAAAAAAkxG+AQAAAAAwGeEbAAAAAACTEb4BAAAAADAZ4RsAAAAAAJMRvgEAAAAAMBnhGwAAAAAAkxG+AQAAAAAwGeEbAAAAAACTEb4BAAAAADAZ4RsAAAAAAJMRvgEAAAAAMBnhGwAAAAAAkxG+AQAAAAAwGeEbAAAAAACTEb4BAAAAADAZ4RsAAAAAAJMRvgEAAAAAMBnhGwAAAAAAkxG+AQAAAAAwGeEbAAAAAACTEb4BAAAAADAZ4RsAAAAAAJMRvgEAAAAAMBnhGwAAAAAAkxG+AQAAAAAwGeEbAAAAAACTEb4BAAAAADAZ4RsAAAAAAJOVufCdkpKin3/+WcnJya4uBQAAAACAEuGy8J2SkqIuXbooNjbWWLZ161b16NFDr776qjp37qytW7e6qjwAAAAAAEqMlys2mpycrNGjRysuLs5Ydu7cOUVERGjVqlVq0qSJIiMj9dprr6lPnz6uKBEAAAAAgBLjkpnv8ePHq3fv3g7L0tPTNWnSJDVp0kSS1LRpU509e9YV5QEAAAAAUKJcEr4jIiI0dOhQh2VXX321+vfvL0nKzs7WkiVL1KNHD1eUBwAAAABAiXLJYeehoaGXfO7AgQN6+OGH5e3trU8++aRY41ssxa0MRWXf1+xzlGf0OYrD3fqlPPS5O9eO0lEe+hwoDH1e+pzd1y4J35cTFhamZcuWadasWQoPD9fbb79d5DGCggJMqAyXwz5HRUCfozCBgX7G9+Bg9+wXd+vz8rDPUfrcrc+B4qDPy54yF74tFouaNWummTNnqnPnzjp79qyqVatWpDFOn06VzWZSgXBgseT9YrPPUZ7R53BWSkq68T0pKdXF1RSNu/a5O+9zlD537XOgKOjz0mff54UpM+F7165d2rFjhyZOnChJ8vT0lCR5eBT9tHSbTTRaKWOfoyKgz1EU7tor7tzn7lo3Sp879zngLPq87Ckz4btBgwYaM2aMrr32Wt1+++2aO3euOnbsqIAADpcAAAAAALg3l1ztvCC1atXSvHnztHz5cvXp00cXLlzQ7NmzXV0WAAAAAABXzKUz39HR0Q6Pb7vtNt12220uqgYAAAAAAHOUmZlvAAAAAADKK8I3AAAAAAAmI3wDAAAAAGAywjcAAAAAACYjfAMAAAAAYDLCNwAAAAAAJiN8AwAAAABgMsI3AAAAAAAmI3wDAAAAAGAywjcAAAAAACYjfAMAAAAAYDLCNwAAAAAAJiN8AwAAAABgMsI3AAAAAAAmI3wDAAAAAGAywjcAAAAAACYjfAMAAAAAYDLCNwAAAAAAJiN8AwAAAABgMsI3AAAAAAAmI3wDAAAAAGAywjcAAAAAACYjfAMAAAAAYDLCNwAAAAAAJiN8AwAAAABgMsI3AAAAAAAmI3wDAAAAAGAywjcAAAAAACYjfAMAAAAAYDLCNwAAAAAAJiN8AwAAAABgMsI3AAAAAAAm83J1AQAAlCch/hYl/LJNAWmHXV1Kkf1X2ZK8XV1GkSQfO6YQf4ury6hwvo/aqDOxf5oydkxMjNLSUk0ZW5J8fLyUlZVjytj+/gEKDQ01Zezqda/TDV3vNmVsFIw+Lxh9XnyEbwAASkhOTo4eb+ejB9MWSL+4upqK4QZJv7fzkb+/v6tLqTAOHTqoHxaO1JROvuZsIPB/X+4qzZxhpyzMVOA1zdW4cRNzNgAH9Hkh6PNisdhsNpuriyhpSUmpKn/vqmyyWKTg4AD2Oco1+hxFsX/3Z/KznnN1GUV2/PgxTZ8eoUmTJuuaa+q5upwi8aoeqtBmN7q6jAqFGcGCMSNYvtDnBaPP87P/v2Kh6xG+cSUIJagI6HNUBHv3/qpu3W7XF1/sUMuWrV1dDmAK/p6jIqDPS5+z4ZsLrgEAAAAAYDLCNwAAAAAAJiN8AwAAAABgMsI3AAAAAAAmI3wDAAAAAGAywjcAAAAAACYjfAMAAAAAYDKXhe+UlBR16dJFsbGxxrIvvvhCXbt2VbNmzTRo0CAdOXLEVeUBAAAAAFBiXBK+k5OTNWrUKMXFxRnLjh8/rkmTJmnChAnasWOHateurRdffNEV5QEAAAAAUKJcEr7Hjx+v3r17Oyw7cuSInnnmGfXu3VvBwcG6//77tX//fleUBwAAAABAifJyxUYjIiIUGhqq6dOnG8s6d+7ssM7Ro0dVr1690i4NAAAAAIAS55LwHRoaetnns7KytGTJEg0bNqxY41ssxXoZisG+r9nnKM/oc1QEF/c5vY7yir/nqAjo89Ln7L52SfguzNy5c1WlShUNHjy4WK8PCgoo4YpQGPY5KgL6HOVZ9ep+xvfgYHod5Rt/z1ER0OdlT5kL399++63WrFmjdevWydvbu1hjnD6dKputhAtDgSyWvF9s9jnKM/ocFcGZM+nG96SkVBdXA5iDv+eoCOjz0mff54UpU+E7JiZGzz77rKZMmaJGjRoVexybTTRaKWOfoyKgz1Ge2XubPkdFQJ+jIqDPy54yE74zMjL0+OOPq1u3buratavS0/M+ga9SpYosnLAAAAAAAHBjZSZ879y5U0eOHNGRI0e0bt06Y3lUVJTq1q3rwsoAAAAAALgyLg3f0dHRxs/dunVzeAwAAAAAQHnh4eoCAAAAAAAo7wjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMnKzK3GAADA5f3111GdO3fWlLEPHYp2+F7SqlatpmuvrW/K2AAAuAPCNwAAbuD06dO66aY2slqtpm5n9OjHTBnX09NT+/cfVlBQkCnjAwBQ1hG+AQBwA0FBQdq9+xfTZr7zZEnyMWXkqlWrEbwBABUa4RsAADdh5mHbFosUHBygpKRU2WymbQYAgAqLC64BAAAAAGAywjcAAAAAACYjfAMAAAAAYDLCNwAAAAAAJiN8AwAAAABgMsI3AAAAAAAmI3wDAAAAAGAywjcAAAAAACYjfAMAAAAAYDLCNwAAAAAAJiN8AwAAAABgMsI3AAAAAAAmI3wDAAAAAGAywjcAAAAAACYjfAMAAAAAYDLCNwAAAAAAJiN8AwAAAABgMi9XF2AGi8XVFVQc9n3NPkd5Rp+jIqDPURHQ56gI6PPS5+y+tthsNpu5pQAAAAAAULFx2DkAAAAAACYjfAMAAAAAYDLCNwAAAAAAJiN8AwAAAABgMsI3AAAAAAAmI3wDAAAAAGAywjcAAAAAACYjfAMAAAAAYDLCNwAAAFAMKSkp+vnnn5WcnOzqUgC4AcJ3BZCQkKCHH35Ybdq00dChQ5WYmOjqkgp08OBB3XvvverQoYNmzZolm83m6pLgRtylz6W8/1nr0qWLYmNjXV0K3Iy79PkXX3yhrl27qlmzZho0aJCOHDni6pLgRtylz7du3aoePXro1VdfVefOnbV161ZXlwQ34i59frERI0Zow4YNri7DrRG+K4Bnn31WderU0ebNm1W3bl29+OKLri4pn6ysLI0aNUrNmzfX+vXrdeTIEX65USTu0OeSlJycrFGjRikuLs7VpcANuUOfHz9+XJMmTdKECRO0Y8cO1a5du0zWibLLHfr83LlzioiI0KpVqxQZGal//vOfeu2111xdFtyIO/T5xTZv3qydO3e6ugz3Z0O5dvjwYVvz5s1tqampNpvNZouNjbU1adLEeFxWfP7557YOHTrYzp8/b7PZbLY///zTNmTIEBdXBXfhLn1us9lsQ4cOtS1btszWpEkTW0xMjKvLgRtxlz7/8ssvbatXrzYe79q1y9a8eXMXVgR34i59Hh8fb9u0aZPx+M8//7S1adPGhRXBnbhLn9ulpKTYbrnlFlvPnj1t69evd3U5bo2Z73Ju7969Cg0Nlb+/vyQpJCREjz32mLKysiRJL7zwgt566y1t2rRJPXv21Pvvv2+89uDBg7r//vvVrl07PfbYY0pISJAk7dmzR126dHHYTlhYmGJjY7VhwwYNHDhQo0ePVrt27TRixAidPHmy0DoPHDigVq1aqXLlysZ4HKYIZ7lLn0tSRESEhg4dWhJvGxWMu/R5586ddf/99xuPjx49qnr16l3x+0fF4C59fvXVV6t///6SpOzsbC1ZskQ9evQokX2A8s9d+txu1qxZ6tatm1q3bn2F7xyE73Lu1KlTql69uvHY09NTzz77rGrUqGEs27lzpz744AOFh4erW7dukqT09HQNHz5cHTt21ObNm3X11VfriSeekNVqLXSb+/btU5s2bRQZGSkfHx+98sorhb4mLS1NdevWNR5bLBZ5eHjo7NmzRXi3qKjcpc8lKTQ0tGhvDvgfd+pzu6ysLC1ZskQPPPBAkV6Hisvd+vzAgQPq2LGjvv32W02aNMn5N4oKzZ36fPfu3dq1a5eee+65or1JFIjwXc7l5OTIwyPvn3nGjBlq37692rdvr+joaGOdmJgYvfvuu+rUqZNCQkIkSV999ZX8/Pw0duxY1alTRy+99JL++usv7d27t9Bt2j+9Cw0N1bhx47R9+3bl5ORc9jWenp7y8fFxWObr66uMjIyivmVUQO7S58CVcMc+nzt3rqpUqaLBgwcX8d2ionK3Pg8LC9OyZcvUqFEjhYeHF+MdoyJylz7PzMzUK6+8oilTphiz9LgyhO9yLiAgQOfOnZMkjRo1SpGRkbJarQ6fkA0YMEABAQEOrztx4oTDTLSPj4+uuuoqnThxIt82Lly44PA4JCREFotFklSrVi3l5ubqzJkzl62zWrVq+W7TkZ6eLm9v78LfJCo8d+lz4Eq4W59/++23WrNmjV5//XX+lsNp7tbnFotFzZo108yZMxUVFcURe3CKu/T5ggUL1KJFC3Xq1Kkobw+X4eXqAmCu6667TkePHlVaWpoCAwNVuXJlnT9/3mGdKlWq5Htd7dq1HW6DlJmZqZMnT6p27drKzMxUbm6u8dz+/fsdXhsfHy+r1SoPDw/Fx8fLy8tLgYGBl63z+uuv10cffWQ8jo2NVVZWlqpVq1ak94uKyV36HLgS7tTnMTExevbZZzVlyhQ1atSoqG8VFZi79PmuXbu0Y8cOTZw4UVLeEXySjNlM4HLcpc+3bNmilJQUtW/fXpKUkZGhTz75RHv37tWUKVOK+rYhZr7LvbZt26p+/fp65ZVXFBsbq7ffftup+2d36tRJ6enpmj9/vuLi4jRt2jRde+21uv766xUSEqJTp07p+PHjOn/+vN566y2H1548eVKLFi1STEyM3n77bXXt2tX4j9KldOjQQampqYqMjJQkvfPOO7rlllsKfR0guU+fA1fCXfo8IyNDjz/+uLp166auXbsqPT1d6enpTtUKuEufN2jQQGvXrtXatWt14sQJvf766+rYsWO+mUqgIO7S56tXr9aWLVsUGRmpyMhIdenSRU8++aSefPLJK3r/FRnhu5zz8PDQggULFB8fr379+unYsWO6+uqrC32dn5+fFi9erJ07d6pfv36Kj4/XggUL5OHhoWuuuUZDhw7VAw88oAceeECjR492eG3r1q31559/6q677lJ2drYmT55c6Pa8vLwUERGhV155Rbfccos+/fRTTZgwodjvGxWLu/Q5cCXcpc937typI0eOaN26dWrbtq3xxb3t4Qx36fNatWpp3rx5Wr58ufr06aMLFy5o9uzZxX7fqFjcpc9DQkJUt25d46tKlSoKDAx0uDAcisZi46NolKANGzZo48aNWrlyZbFen5iYqH379qlt27b8YqPMutI+B9wBfY6KgD5HRUCflx2c840ypVatWqpVq5arywAAAACAEsXMNwAAAAAAJuOcbwAAAAAATEb4BgAAAADAZIRvAAAAAABMRvgGAAAAAMBkhG8AAAAAAExG+AYAAAAAwGSEbwAAAAAATEb4BgAAAADAZIRvAAAAAABM9n/Gxf/RjBHI9gAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "置信区间分析:\n",
      "   组ID         均值      5%分位数     95%分位数\n",
      "0    0  16.212858  16.285715  16.285715\n",
      "1    1  13.817146  13.857146  13.857148\n",
      "2    2  14.395606  12.285715  17.642863\n",
      "3    3  12.352589  11.780189  12.285716\n",
      "4    4  12.285715  12.285715  12.285715\n"
     ]
    }
   ],
   "execution_count": 239
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T02:25:53.739410Z",
     "start_time": "2025-09-07T02:25:53.729279Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 初始化存储结构（保持原有逻辑不变）\n",
    "t_recs = []  # 存储每组推荐检测时点\n",
    "bmi_mean_per_group = []  # 存储每组BMI中值\n",
    "patient_count_per_group = []  # 存储每组孕妇数量\n",
    "\n",
    "# 提取每组的推荐检测时点、BMI中值和孕妇数量\n",
    "for sim_idx in range(len(results['group_summary'])):\n",
    "    sim_summary = results['group_summary'][sim_idx]\n",
    "    t_rec = []\n",
    "    bmi_avg = []\n",
    "    patient_count = []\n",
    "\n",
    "    for group_id in sorted(set([row['组ID'] for row in sim_summary])):\n",
    "        group_data = [row for row in sim_summary if row['组ID'] == group_id]\n",
    "        if group_data:\n",
    "            t_rec.append(group_data[0]['推荐检测时点'])\n",
    "            bmi_avg.append(group_data[0]['BMI中值'])\n",
    "            patient_count.append(group_data[0]['孕妇数量'])\n",
    "        else:\n",
    "            t_rec.append(np.nan)\n",
    "            bmi_avg.append(np.nan)\n",
    "            patient_count.append(np.nan)\n",
    "\n",
    "    # 填充缺失组（如组数不一致）\n",
    "    while len(t_rec) < 4:  # 假设最多4组\n",
    "        t_rec.append(np.nan)\n",
    "        bmi_avg.append(np.nan)\n",
    "        patient_count.append(np.nan)\n",
    "\n",
    "    t_recs.append(t_rec)\n",
    "    bmi_mean_per_group.append(bmi_avg)\n",
    "    patient_count_per_group.append(patient_count)\n",
    "\n",
    "# 计算每组推荐检测时间波动（均值 ± 标准差）\n",
    "print(\"\\n各组推荐检测时间波动（均值 ± 标准差）:\")\n",
    "for i in range(4):  # 固定最大组数为4\n",
    "    t_list = [t_recs[sim][i] for sim in range(len(t_recs)) if i < len(t_recs[sim]) and not np.isnan(t_recs[sim][i])]\n",
    "    if len(t_list) > 0:\n",
    "        mean_t = np.mean(t_list)\n",
    "        std_t = np.std(t_list)\n",
    "        print(f\"组 {i}: {mean_t:.2f} ± {std_t:.2f} 周\")\n",
    "    else:\n",
    "        print(f\"组 {i}: 无有效数据\")\n",
    "\n",
    "# 计算每组特征稳定性（加噪后平均）\n",
    "print(\"\\n各组特征稳定性（加噪后平均）:\")\n",
    "for i in range(4):  # 固定最大组数为4\n",
    "    bmi_list = [bmi_mean_per_group[sim][i] for sim in range(len(bmi_mean_per_group)) if i < len(bmi_mean_per_group[sim]) and not np.isnan(bmi_mean_per_group[sim][i])]\n",
    "    patient_list = [patient_count_per_group[sim][i] for sim in range(len(patient_count_per_group)) if i < len(patient_count_per_group[sim]) and not np.isnan(patient_count_per_group[sim][i])]\n",
    "\n",
    "    if len(bmi_list) > 0 and len(patient_list) > 0:\n",
    "        avg_bmi = np.mean(bmi_list)\n",
    "        avg_patient = np.mean(patient_list)\n",
    "        print(f\"组 {i}: 平均BMI中值={avg_bmi:.2f}, 平均孕妇数量={avg_patient:.0f}\")\n",
    "    else:\n",
    "        print(f\"组 {i}: 无有效数据\")"
   ],
   "id": "946df344deb4cf7d",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "各组推荐检测时间波动（均值 ± 标准差）:\n",
      "组 0: 16.21 ± 0.41 周\n",
      "组 1: 13.82 ± 0.24 周\n",
      "组 2: 14.40 ± 2.46 周\n",
      "组 3: 12.35 ± 0.67 周\n",
      "\n",
      "各组特征稳定性（加噪后平均）:\n",
      "组 0: 平均BMI中值=20.97, 平均孕妇数量=9\n",
      "组 1: 平均BMI中值=31.89, 平均孕妇数量=243\n",
      "组 2: 平均BMI中值=41.73, 平均孕妇数量=9\n",
      "组 3: 平均BMI中值=44.41, 平均孕妇数量=4\n"
     ]
    }
   ],
   "execution_count": 240
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
